Home
Softono
y

youngfish42

Professional software vendor delivering innovative solutions on the Softono platform. Specialized in both open-source and proprietary software development.

Visit Website
Total Products
1

Software by youngfish42

Awesome-FL
Open Source

Awesome-FL

# Federated Learning Resources [![Stars](https://img.shields.io/github/stars/youngfish42/Awesome-FL.svg?color=orange)](https://github.com/youngfish42/Awesome-FL/stargazers) [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re) [![License](https://img.shields.io/github/license/youngfish42/Awesome-FL.svg?color=green)](https://github.com/youngfish42/image-registration-resources/blob/master/LICENSE) ![](https://img.shields.io/github/last-commit/youngfish42/Awesome-FL) --- **Table of Contents** - [Papers](#papers) - [FL in top-tier journal](#fl-in-top-tier-journal) - FL in top-tier conference and journal by category - [AI](#fl-in-top-ai-conference-and-journal) [ML](#fl-in-top-ml-conference-and-journal) [DM](#fl-in-top-dm-conference-and-journal) [Secure](#fl-in-top-secure-conference-and-journal) [CV](#fl-in-top-cv-conference-and-journal) [NLP](#fl-in-top-nlp-conference-and-journal) [IR](#fl-in-top-ir-conference-and-journal) [DB](#fl-in-top-db-conference-and-journal) [Network](#fl-in-top-network-conference-and-journal) [System](#fl-in-top-system-conference-and-journal) [Others](#fl-in-top-conference-and-journal-other-fields) - [FL on Graph Data and Graph Neural Networks](#fl-on-graph-data-and-graph-neural-networks) [[dblp]](https://dblp.uni-trier.de/search?q=Federated%20graph%7Csubgraph%7Cgnn) - [FL on Tabular Data](#fl-on-tabular-data) [[dblp]](https://dblp.org/search?q=federate%20tree%7Cboost%7Cbagging%7Cgbdt%7Ctabular%7Cforest%7CXGBoost) - [Framework](#framework) - [Datasets](#datasets) - [Surveys](#surveys) - [Tutorials and Courses](#tutorials-and-courses) - Key Conferences/Workshops/Journals - [Workshops](#workshops) [Special Issues](#journal-special-issues) [Special Tracks](#conference-special-tracks) - [Update log](#update-log) - [Acknowledgments](#acknowledgments) - [Citation](#citation) We use another project to automatically track updates to FL papers, click on [FL-paper-update-tracker](https://github.com/youngfish42/FL-paper-update-tracker) if you need it. Please note that if this page does not display the full content, **please visit [the official homepage](https://youngfish42.github.io/Awesome-FL) for full information.** **More items will be added to the repository**. Please feel free to suggest other key resources by opening an [issue](https://github.com/youngfish42/Awesome-FL/issues) report, submitting a pull request, or dropping me an email @ ([[email protected]](mailto:[email protected])). If you want to communicate with more friends in the field of federated learning, please join the QQ group [联邦学习交流群], the group number is 833638275. Enjoy reading! **Repository Update Notice** > 2024/09/30 > > > > Dear Users, We would like to inform you of a few changes that will affect this open source repository. The owner and principal contributor [@youngfish42](https://github.com/youngfish42) has successfully completed his doctoral studies 🎓 as of September 30, 2024, and has since shifted his research focus. This change in circumstances will impact the frequency and extent of updates to the repository's paper list. > > Instead of the previous regular updates, we anticipate that the paper list will now be updated on a monthly or quarterly basis. Furthermore, the depth of these updates will be reduced. For instance, updates related to the author's institution and open source code will no longer be actively maintained. > > We understand that this might affect the value you derive from this repository. Therefore, we humbly invite more contributors to participate in updating the content. This collaborative effort will ensure that the repository remains a valuable resource for everyone. > > We appreciate your understanding and look forward to your continued support and contributions. > > > > Best Regards, > > 白小鱼 (youngfish) > # papers **categories** - Artificial Intelligence (IJCAI, AAAI, AISTATS, ALT, AI) - Machine Learning (NeurIPS, ICML, ICLR, COLT, UAI, Machine Learning, JMLR, TPAMI) - Data Mining (KDD, WSDM) - Secure (S&P, CCS, USENIX Security, NDSS) - Computer Vision (ICCV, CVPR, ECCV, MM, IJCV) - Natural Language Processing (ACL, EMNLP, NAACL, COLING) - Information Retrieval (SIGIR) - Database (SIGMOD, ICDE, VLDB) - Network (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW) - System (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC) - Others (ICSE, FOCS, STOC) <details open> <summary> Events </summary> | Venue | 2024-2020 | before 2020 | | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | [IJCAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AIJCAI%3A) | [25](https://www.ijcai.org/proceedings/2025/), [24](https://www.ijcai.org/proceedings/2024/), [23](https://www.ijcai.org/proceedings/2023/), [22](https://www.ijcai.org/proceedings/2022/), [21](https://www.ijcai.org/proceedings/2021/), [20](https://www.ijcai.org/proceedings/2020/) | [19](https://www.ijcai.org/proceedings/2019/) | | [AAAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AAAAI%3A) | [26](https://dblp.org/db/conf/aaai/aaai2026.html), [25](https://dblp.org/db/conf/aaai/aaai2025.html), [24](https://dblp.org/db/conf/aaai/aaai2024.html), [23](https://dblp.org/db/conf/aaai/aaai2023), [22](https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [21](https://aaai.org/Conferences/AAAI-21/wp-content/uploads/2020/12/AAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [20](https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf) | - | | [AISTATS](https://dblp.uni-trier.de/search?q=federate%20venue%3AAISTATS%3A) | [25](https://proceedings.mlr.press/v258/), [24](http://proceedings.mlr.press/v238/), [23](http://proceedings.mlr.press/v206/), [22](http://proceedings.mlr.press/v151/), [21](http://proceedings.mlr.press/v130/), [20](http://proceedings.mlr.press/v108/) | - | | [ALT](https://dblp.uni-trier.de/search?q=federate%20streamid%3Aconf%2Falt%3A) | 22 | - | | [AI](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fai%3A) (J) | 26, 25, 23 | - | | [NeurIPS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANeurIPS%3A) | [24](https://openreview.net/group?id=NeurIPS.cc/2024/Conference#tab-accept-oral), [23](https://openreview.net/group?id=NeurIPS.cc/2023/Conference#tab-accept-oral), [22](https://papers.nips.cc/paper_files/paper/2022), [21](https://papers.nips.cc/paper/2021), [20](https://papers.nips.cc/paper/2020) | [18](https://papers.nips.cc/paper/2018), [17](https://papers.nips.cc/paper/17) | | [ICML](https://dblp.uni-trier.de/search?q=federate%20venue%3AICML%3A) | [25](https://icml.cc/Conferences/2025/Schedule?type=Poster), [24](https://icml.cc/Conferences/2024/Schedule?type=Poster), [23](https://icml.cc/Conferences/2023/Schedule?type=Poster), [22](https://icml.cc/Conferences/2022/Schedule?type=Poster), [21](https://icml.cc/Conferences/2021/Schedule?type=Poster), [20](https://icml.cc/Conferences/2020/Schedule?type=Poster) | [19](https://icml.cc/Conferences/2019/Schedule?type=Poster) | | [ICLR](https://dblp.uni-trier.de/search?q=federate%20venue%3AICLR%3A) | [25](https://openreview.net/group?id=ICLR.cc/2025), [24](https://openreview.net/group?id=ICLR.cc/2024/Conference), [23](https://openreview.net/group?id=ICLR.cc/2023/Conference), [22](https://openreview.net/group?id=ICLR.cc/2022/Conference), [21](https://openreview.net/group?id=ICLR.cc/2021/Conference), [20](https://openreview.net/group?id=ICLR.cc/2020/Conference) | - | | [COLT](https://dblp.org/search?q=federated%20venue%3ACOLT%3A) | [23](https://proceedings.mlr.press/v195/) | - | | [UAI](https://dblp.org/search?q=federated%20venue%3AUAI%3A) | [25](https://www.auai.org/uai2025/accepted_papers), [24](https://www.auai.org/uai2024/accepted_papers), [23](https://www.auai.org/uai2023/accepted_papers), [22](https://www.auai.org/uai2022/accepted_papers), [21](https://www.auai.org/uai2021/accepted_papers) | - | | [Machine Learning](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fml%3A) (J) | 26, 25, 24, 23, 22 | - | | [JMLR](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fjmlr%3A) (J) | 25, 24, 23, 22 | - | | [TPAMI](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fpami%3A) (J) | 26, 25, 24, 23, 22 | - | | [KDD](https://dblp.uni-trier.de/search?q=federate%20venue%3AKDD%3A) | [26](https://dl.acm.org/doi/proceedings/10.1145/3770854), [25](https://dl.acm.org/doi/proceedings/10.1145/3690624), [24](https://dl.acm.org/doi/proceedings/10.1145/3637528), [23](https://dl.acm.org/doi/proceedings/10.1145/3580305), [22](https://kdd.org/kdd2022/paperRT.html), [21](https://kdd.org/kdd2021/accepted-papers/index), [20](https://www.kdd.org/kdd2020/accepted-papers) | | | [WSDM](https://dblp.uni-trier.de/search?q=federate%20venue%3AWSDM%3A) | [26](https://dl.acm.org/doi/proceedings/10.1145/3773966),[25](https://www.wsdm-conference.org/2025/accepted-papers/), [24](https://www.wsdm-conference.org/2024/accepted-papers/), [23](https://www.wsdm-conference.org/2023/program/accepted-papers), [22](https://www.wsdm-conference.org/2022/accepted-papers/), [21](https://www.wsdm-conference.org/2021/accepted-papers.php) | [19](https://www.wsdm-conference.org/2019/accepted-papers.php) | | [S&P](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsp%3A) | [25](https://sp2025.ieee-security.org/program-papers.html), [24](https://sp2024.ieee-security.org/program-papers.html), [23](https://sp2023.ieee-security.org/program-papers.html), [22](https://www.ieee-security.org/TC/SP2022/program-papers.html) | [19](https://www.ieee-security.org/TC/SP2019/program-papers.html) | | [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) | [25](https://dl.acm.org/doi/proceedings/10.1145/3719027), [24](https://dl.acm.org/doi/proceedings/10.1145/3658644), [23](https://dl.acm.org/doi/proceedings/10.1145/3576915), [22](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [21](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [19](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/) | [17](https://acmccs.github.io/papers/) | | [USENIX Security](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fuss%3A) | [25](https://www.usenix.org/conference/usenixsecurity25/technical-sessions), [24](https://www.usenix.org/conference/usenixsecurity24/technical-sessions), [23](https://www.usenix.org/conference/usenixsecurity23/technical-sessions), [22](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [20](https://www.usenix.org/conference/usenixsecurity20/technical-sessions) | - | | [NDSS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANDSS%3A) | [26](https://www.ndss-symposium.org/ndss2026/accepted-papers/), [25](https://www.ndss-symposium.org/ndss2025/accepted-papers/), [24](https://www.ndss-symposium.org/ndss2024/accepted-papers/), [23](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [22](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [21](https://www.ndss-symposium.org/ndss2021/accepted-papers/) | - | | [CVPR](https://dblp.uni-trier.de/search?q=federate%20venue%3ACVPR%3A) | [25](https://openaccess.thecvf.com/CVPR2025?day=all), [24](https://openaccess.thecvf.com/CVPR2024?day=all), [23](https://openaccess.thecvf.com/CVPR2023?day=all), [22](https://openaccess.thecvf.com/CVPR2022), [21](https://openaccess.thecvf.com/CVPR2021?day=all) | - | | [ICCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AICCV%3A) | [23](https://openaccess.thecvf.com/ICCV2023?day=all),[21](https://openaccess.thecvf.com/ICCV2021?day=all) | - | | [ECCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AECCV%3A) | [24](https://www.ecva.net/papers.php), [22](https://www.ecva.net/papers.php), [20](https://www.ecva.net/papers.php) | - | | [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) | [25](https://dl.acm.org/doi/proceedings/10.1145/3746027), [24](https://dl.acm.org/doi/proceedings/10.1145/3664647), [23](https://dl.acm.org/doi/proceedings/10.1145/3581783), [22](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [21](https://2021.acmmm.org/main-track-list), [20](https://2020.acmmm.org/main-track-list.html) | - | | [IJCV](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fijcv%3A) (J) | 25, 24 | - | | [ACL](https://dblp.uni-trier.de/search?q=federate%20venue%3AACL%3A) | [25](https://aclanthology.org/events/acl-2025/), [24](https://aclanthology.org/events/acl-2024/), [23](https://aclanthology.org/events/acl-2023/), [22](https://aclanthology.org/events/acl-2022/), [21](https://aclanthology.org/events/acl-2021/) | [19](https://aclanthology.org/events/acl-2019/) | | [NAACL](https://dblp.uni-trier.de/search?q=federate%20venue%3ANAACL-HLT%3A) | [24](https://aclanthology.org/events/naacl-2024/), [22](https://aclanthology.org/events/naacl-2022/), [21](https://aclanthology.org/events/naacl-2021/) | - | | [EMNLP](https://dblp.uni-trier.de/search?q=federate%20venue%3AEMNLP%3A) | [25](https://aclanthology.org/events/emnlp-2025/), [24](https://aclanthology.org/events/emnlp-2024/), [23](https://aclanthology.org/events/emnlp-2023/), [22](https://aclanthology.org/events/emnlp-2022/), [21](https://aclanthology.org/events/emnlp-2021/), [20](https://aclanthology.org/events/emnlp-2020/) | - | | [COLING](https://dblp.uni-trier.de/search?q=federate%20venue%3ACOLING%3A) | [25](https://aclanthology.org/volumes/2025.coling-main/), [20](https://aclanthology.org/events/coling-2020/) | - | | [SIGIR](https://dblp.uni-trier.de/search?q=federate%20venue%3ASIGIR%3A) | [25](https://dl.acm.org/doi/proceedings/10.1145/3726302), [24](https://dl.acm.org/doi/proceedings/10.1145/3626772), [23](https://dl.acm.org/doi/proceedings/10.1145/3539618), [22](https://dl.acm.org/doi/proceedings/10.1145/3477495), [21](https://dl.acm.org/doi/proceedings/10.1145/3404835), [20](https://dl.acm.org/doi/proceedings/10.1145/3397271) | - | | [SIGMOD](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsigmod%3A) | [25](https://2025.sigmod.org/sigmod_papers.shtml), [24](https://2024.sigmod.org/), [23](https://2023.sigmod.org/sigmod_research_list.shtml), [22](https://2022.sigmod.org/sigmod_research_list.shtml), [21](https://2021.sigmod.org/sigmod_research_list.shtml) | - | | [ICDE](https://dblp.uni-trier.de/search?q=federate%20venue%3AICDE%3A) | [25](https://ieee-icde.org/2025/research-papers/), [24](https://icde2024.github.io/), [23](https://icde2023.ics.uci.edu/papers-research-track/), [22](https://icde2022.ieeecomputer.my/accepted-research-track/), [21](https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding) | - | | [VLDB](https://dblp.org/search?q=federated%20streamid%3Ajournals%2Fpvldb%3A) | [25](https://vldb.org/pvldb/volumes/18), [24](https://vldb.org/pvldb/volumes/17), [23](https://vldb.org/pvldb/volumes/17), [22](https://vldb.org/pvldb/vol16-volume-info/), [21](https://vldb.org/pvldb/vol15-volume-info/), [21](http://www.vldb.org/pvldb/vol14/), [20](http://vldb.org/pvldb/vol13-volume-info/) | - | | [SIGCOMM](https://dblp.uni-trier.de/search?q=federate%20venue%3ASIGCOMM%3A) | 25 | - | | [INFOCOM](https://dblp.uni-trier.de/search?q=federate%20venue%3AINFOCOM%3A) | [25](https://infocom2025.ieee-infocom.org/program/accepted-paper-list-main-conference), [24](https://infocom2024.ieee-infocom.org/program/accepted-paper-list-main-conference), [23](https://infocom2023.ieee-infocom.org/program/accepted-paper-list-main-conference), [22](https://infocom2022.ieee-infocom.org/program/accepted-paper-list-main-conference), [21](https://infocom2021.ieee-infocom.org/accepted-paper-list-main-conference.html), [20](https://infocom2020.ieee-infocom.org/accepted-paper-list-main-conference.html) | [19](https://infocom2019.ieee-infocom.org/accepted-paper-list-main-conference.html), 18 | | [MobiCom](https://dblp.uni-trier.de/search?q=federate%20venue%3AMobiCom%3A) | [25](https://www.sigmobile.org/mobicom/2025/accepted.html), [24](https://www.sigmobile.org/mobicom/2024/accepted.html), [23](https://www.sigmobile.org/mobicom/2023/accepted.html), [22](https://www.sigmobile.org/mobicom/2022/accepted.html), [21](https://www.sigmobile.org/mobicom/2021/accepted.html), [20](https://www.sigmobile.org/mobicom/2020/accepted.php) | | | [NSDI](https://dblp.uni-trier.de/search?q=federate%20venue%3ANSDI%3A) | [25](https://www.usenix.org/conference/nsdi25/technical-sessions), 23([1](https://www.usenix.org/conference/nsdi23/spring-accepted-papers), [2](https://www.usenix.org/conference/nsdi23/fall-accepted-papers)) | - | | [WWW](https://dblp.uni-trier.de/search?q=federate%20venue%3AWWW%3A) | [26](https://dl.acm.org/doi/proceedings/10.1145/3774904), [25](https://dl.acm.org/doi/proceedings/10.1145/3696410), [24](https://www2024.thewebconf.org/accepted/research-tracks/), [23](https://www2023.thewebconf.org/program/accepted-papers/), [22](https://www2022.thewebconf.org/accepted-papers/), [21](https://www2021.thewebconf.org/program/papers-program/links/index.html) | | | [OSDI](https://dblp.org/search?q=federated%20venue%3AOSDI%3A) | 21 | - | | [SOSP](https://dblp.org/search?q=federated%20venue%3ASOSP%3A) | 21 | - | | [ISCA](https://dblp.org/search?q=federated%20venue%3AISCA%3A) | [24](https://www.iscaconf.org/isca2024/program/) | - | | [MLSys](https://dblp.org/search?q=federated%20venue%3AMLSys%3A) | [25](https://proceedings.mlsys.org/paper_files/paper/2025), [24](https://proceedings.mlsys.org/paper_files/paper/2024), [23](https://proceedings.mlsys.org/paper_files/paper/2023), [22](https://proceedings.mlsys.org/paper_files/paper/2022), [20](https://proceedings.mlsys.org/paper_files/paper/2020) | [19](https://proceedings.mlsys.org/paper_files/paper/2019) | | [EuroSys](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Feurosys%3A) | [26](https://2026.eurosys.org/papers.html#papers), [25](https://2025.eurosys.org/accepted-papers.html), [24](https://2024.eurosys.org/accepted-papers.html), [23](https://2023.eurosys.org/accepted-papers.html), 22, 21, 20 | | | [TPDS](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Ftpds%3A) (J) | 26, 25, 24, 23, 22, 21, 20 | - | | [DAC](https://dblp.uni-trier.de/search?q=federate%20venue%3ADAC%3A) | 25, 24, 22, 21 | - | | [TOCS](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftocs%3A) | - | - | | [TOS](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftos%3A) | - | - | | [TCAD](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftcad%3A) | 26, 25, 24, 23, 22, 21 | - | | [TC](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftc%3A) | 26, 25, 24, 23, 22, 21 | - | | [ICSE](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Ficse%3A) | [25](https://conf.researchr.org/track/icse-2025/icse-2025-research-track), [23](https://conf.researchr.org/track/icse-2023/icse-2023-technical-track?#event-overview), 21 | - | | [FOCS](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ffocs%3A) | - | - | | [STOC](https://dblp.uni-trier.de/search?q=federate%20streamid%3Aconf%2Fstoc%3A) | - | - | </details> **keywords** Statistics: :fire: code is available & stars >= 100 | :star: citation >= 50 | :mortar_board: Top-tier venue **`kg.`**: Knowledge Graph | **`data.`**: dataset  |   **`surv.`**: survey ## fl in top-tier journal Papers of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to [WOS](https://www.webofscience.com/wos/woscc/summary/ed3f4552-5450-4de7-bf2c-55d01e20d5de-4301299e/relevance/1) search engine. <details open> <summary>fl in top-tier journal</summary> |Title | Venue | Year | Materials| | ------------------------------------------------------------ | --------------------- | ---- | ------------------------------------------------------------ | | Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption | Nat. Mach. Intell. | 2025 | [[PUB](https://www.nature.com/articles/s42256-025-01107-6)] [[PDF](https://arxiv.org/abs/2408.06197)] [[CODE](https://github.com/siyang-jiang/Lancelot)] | | Incentivizing inclusive contributions in model sharing markets | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-62959-5)] [[CODE](https://github.com/19dx/iPFL)] | | FedECA: federated external control arms for causal inference with time-to-event data in distributed settings | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-62525-z)] [[CODE](https://github.com/owkin/fedeca)] | | Privacy-preserving multicenter differential protein abundance analysis with FedProt | Nat. Comput. Sci. | 2025 | [[PUB](https://www.nature.com/articles/s43588-025-00832-7)] [[CODE](https://github.com/Freddsle/FedProt)] | | Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-60466-1)] [[CODE](https://github.com/mlcommons/medperf/tree/fets-challenge)] | | A fully open AI foundation model applied to chest radiography | Nature | 2025 | [[PUB](https://www.nature.com/articles/s41586-025-09079-8)] [[CODE](https://github.com/jlianglab/Ark)] | | Federated learning using a memristor compute-in-memory chip with in situ physical unclonable function and true random number generator | Nat. Electron. | 2025 | [[PUB](https://www.nature.com/articles/s41928-025-01390-6)] | | A framework reforming personalized Internet of Things by federated meta-learning | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-59217-z)] [[CODE](https://github.com/IntelligentSystemsLab/generic_and_open_learning_federator/)] | | Achieving flexible fairness metrics in federated medical imaging | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-58549-0)] [[CODE](https://zenodo.org/records/15203267)] | | Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-58055-3)] [[CODE](https://github.com/paridis-11/DynamicFL)] | | Data-driven federated learning in drug discovery with knowledge distillation | Nat. Mach. Intell. | 2025 | [[PUB](https://www.nature.com/articles/s42256-025-00991-2)] [[CODE](https://github.com/LhasaLimited/FLuID_POC)] | | Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-56510-9)] | | Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-56412-w)] [[新闻](https://ic.pku.edu.cn/kxyj/kycg1/d2c084006150492c93ae3e6b0cb1d7df.htm)] | | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-53431-x)] [[CODE](https://github.com/SICC-Group/MatSwarm)] | | Introducing edge intelligence to smart meters via federated split learning | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-53352-9)] [[新闻](https://www.ces.org.cn/html/report/24110829-1.htm)] | | An international study presenting a federated learning AI platform for pediatric brain tumors | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-51172-5)] [[CODE](https://github.com/edhlee/FLPedBrain)] | | PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data | Science Advances | 2024 | [[PUB](https://www.science.org/doi/10.1126/sciadv.adh8601)] [[CODE](https://github.com/JoshuaChou2018/PPML-Omics)] | | Federated learning is not a cure-all for data ethics | Nat. Mach. Intell.(Comment) | 2024 | [[PUB](https://www.nature.com/articles/s42256-024-00813-x)] | | Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-44946-4)] [[CODE](https://github.com/baofengguat/RFLM-project/)] | | Selective knowledge sharing for privacy-preserving federated distillation without a good teacher | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-023-44383-9)] [[PDF](https://arxiv.org/abs/2304.01731)] [[CODE](https://github.com/shaojiawei07/Selective-FD)] | | A federated learning system for precision oncology in Europe: DigiONE | Nat. Med. (Comment) | 2024 | [[PUB](https://www.nature.com/articles/s41591-023-02715-8)] | | Multi-client distributed blind quantum computation with the Qline architecture | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43617-0)] [[PDF](https://arxiv.org/abs/2306.05195)] | | Device-independent quantum randomness–enhanced zero-knowledge proof | PNAS | 2023 | [[PUB](https://www.pnas.org/doi/10.1073/pnas.2205463120)] [[PDF](https://arxiv.org/abs/2111.06717)] [[新闻](https://www.nsfc.gov.cn/publish/portal0/tab448/info90817.htm)] | | Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43883-y)] | | Advocating for neurodata privacy and neurotechnology regulation | Nat. Protoc. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41596-023-00873-0)] | | Federated benchmarking of medical artificial intelligence with MedPerf | Nat. Mach. Intell. | 2023 | [[PUB](https://www.nature.com/articles/s42256-023-00652-2)] [[PDF](https://arxiv.org/abs/2110.01406)] [[CODE](https://github.com/mlcommons/MedPerf)] | | Algorithmic fairness in artificial intelligence for medicine and healthcare | Nat. Biomed. Eng. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41551-023-01056-8)] [[PDF](https://arxiv.org/abs/2110.00603)] | | Differentially private knowledge transfer for federated learning | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-38794-x)] [[CODE](https://github.com/taoqi98/PrivateKT)] | | Decentralized federated learning through proxy model sharing | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-38569-4)] [[PDF](https://arxiv.org/abs/2111.11343)] [[CODE](https://github.com/layer6ai-labs/ProxyFL)] | | Federated machine learning in data-protection-compliant research | Nat. Mach. Intell.(Comment) | 2023 | [[PUB](https://www.nature.com/articles/s42256-022-00601-5)] | | Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer | Nat. Med. | 2023 | [[PUB](https://www.nature.com/articles/s41591-022-02155-w)] [[CODE](https://github.com/Substra/substra)] | | Federated learning enables big data for rare cancer boundary detection | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-33407-5)] [[PDF](https://arxiv.org/abs/2204.10836)] [[CODE](https://github.com/FETS-AI/Front-End)] | | Federated learning and Indigenous genomic data sovereignty | Nat. Mach. Intell. (Comment) | 2022 | [[PUB](https://www.nature.com/articles/s42256-022-00551-y)] | | Federated disentangled representation learning for unsupervised brain anomaly detection | Nat. Mach. Intell. | 2022 | [[PUB](https://www.nature.com/articles/s42256-022-00515-2)] [[PDF](https://doi.org/https://doi.org/10.21203/rs.3.rs-722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] | | Shifting machine learning for healthcare from development to deployment and from models to data | Nat. Biomed. Eng. (Review Article) | 2022 | [[PUB](https://www.nature.com/articles/s41551-022-00898-y)] | | A federated graph neural network framework for privacy-preserving personalization | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-30714-9)] [[CODE](https://github.com/wuch15/FedPerGNN)] [[解读](https://zhuanlan.zhihu.com/p/487383715)] | | Communication-efficient federated learning via knowledge distillation | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-29763-x)] [[PDF](https://arxiv.org/abs/2108.13323)] [[CODE](https://zenodo.org/record/6383473)] | | Lead federated neuromorphic learning for wireless edge artificial intelligence | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-32020-w)] [[CODE](https://github.com/GOGODD/FL-EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] | | A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data | Sci. Rep. | 2022 | [[PUB](https://www.nature.com/articles/s41598-022-12833-x)] | | Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence | Nat. Mach. Intell. | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00421-z)] [[PDF](https://arxiv.org/abs/2111.09461)] [[CODE](https://github.com/HUST-EIC-AI-LAB/UCADI)] | | Federated learning for predicting clinical outcomes in patients with COVID-19 | Nat. Med. | 2021 | [[PUB](https://www.nature.com/articles/s41591-021-01506-3)] [[CODE](https://www.nature.com/articles/s41591-021-01506-3#code-availability)] | | Adversarial interference and its mitigations in privacy-preserving collaborative machine learning | Nat. Mach. Intell.(Perspective) | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00390-3)] | | Swarm Learning for decentralized and confidential clinical machine learning :star: | Nature :mortar_board: | 2021 | [[PUB](https://www.nature.com/articles/s41586-021-03583-3)] [[CODE](https://github.com/HewlettPackard/swarm-learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] | | End-to-end privacy preserving deep learning on multi-institutional medical imaging | Nat. Mach. Intell. | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00337-8)] [[CODE](https://doi.org/10.5281/zenodo.4545599)] [[解读](https://zhuanlan.zhihu.com/p/484801505)] | | Communication-efficient federated learning | PANS. | 2021 | [[PUB](https://www.pnas.org/doi/full/10.1073/pnas.2024789118)] [[CODE](https://code.ihub.org.cn/projects/4394/repository/revisions/master/show/PNAS)] | | Breaking medical data sharing boundaries by using synthesized radiographs | Science. Advances. | 2020 | [[PUB](https://www.science.org/doi/10.1126/sciadv.abb7973)] [[CODE](https://github.com/peterhan91/Thorax_GAN)] | | Secure, privacy-preserving and federated machine learning in medical imaging :star: | Nat. Mach. Intell.(Perspective) | 2020 | [[PUB](https://www.nature.com/articles/s42256-020-0186-1)] | <!-- END:fl-in-top-tier-journal --> </details> ## fl in top ai conference and journal Federated Learning papers accepted by top AI(Artificial Intelligence) conference and journal, Including [IJCAI](https://dblp.org/db/conf/ijcai/index.html)(International Joint Conference on Artificial Intelligence), [AAAI](https://dblp.uni-trier.de/db/conf/aaai/index.html)(AAAI Conference on Artificial Intelligence), [AISTATS](https://dblp.uni-trier.de/db/conf/aistats/index.html)(Artificial Intelligence and Statistics), [ALT](https://dblp.org/db/conf/alt/index.html)(International Conference on Algorithmic Learning Theory), [AI](https://dblp.uni-trier.de/db/journals/ai/index.html)(Artificial Intelligence). - [IJCAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AIJCAI%3A) [2025](https://www.ijcai.org/proceedings/2025/), [2024](https://www.ijcai.org/proceedings/2024/), [2023](https://www.ijcai.org/proceedings/2023/), [2022](https://www.ijcai.org/proceedings/2022/), [2021](https://www.ijcai.org/proceedings/2021/), [2020](https://www.ijcai.org/proceedings/2020/), [2019](https://www.ijcai.org/proceedings/2019/) - [AAAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AAAAI%3A) [2026](https://dblp.org/db/conf/aaai/aaai2026.html), [2025](https://dblp.org/db/conf/aaai/aaai2025.html), [2024](https://dblp.org/db/conf/aaai/aaai2024.html), [2023](https://dblp.org/db/conf/aaai/aaai2023), [2022](https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [2021](https://aaai.org/Conferences/AAAI-21/wp-content/uploads/2020/12/AAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [2020](https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf) - [AISTATS](https://dblp.uni-trier.de/search?q=federate%20venue%3AAISTATS%3A) [2025](https://proceedings.mlr.press/v258/), [2024](http://proceedings.mlr.press/v238/), [2023](http://proceedings.mlr.press/v206/), [2022](http://proceedings.mlr.press/v151/), [2021](http://proceedings.mlr.press/v130/), [2020](http://proceedings.mlr.press/v108/) - [ALT](https://dblp.uni-trier.de/search?q=federate%20streamid%3Aconf%2Falt%3A) 2022 - [AI](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fai%3A) 2026, 2025, 2023 <details open> <summary>fl in top ai conference and journal</summary> <!-- START:fl-in-top-ai-conference-and-journal --> <!-- END:fl-in-top-ai-conference-and-journal --> ### 2026 #### AAAI - A Unified Self-Regulating Training Framework for Federated Deep Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v40i32.39946)] - Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach. [[PUB](https://doi.org/10.1609/aaai.v40i33.39991)] - BIQ: Bisection Interval Quantization for Communication-efficient Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i25.39259)] - Breaking Cross-View Associations: Byzantine Model Poisoning Attack against Vertical Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i48.42327)] - Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. [[PUB](https://doi.org/10.1609/aaai.v40i17.38472)] - Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs. [[PUB](https://doi.org/10.1609/aaai.v40i28.39569)] - Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering. [[PUB](https://doi.org/10.1609/aaai.v40i28.39547)] - Class-Aware Active Annotation in Federated Semi-Supervised Learning for Medical Image Classification. [[PUB](https://doi.org/10.1609/aaai.v40i32.39964)] - Communication-Efficient Heterogeneous Federated Learning with Sparse Prototypes in Resource-Constrained Environments. [[PUB](https://doi.org/10.1609/aaai.v40i27.39441)] - CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation. [[PUB](https://doi.org/10.1609/aaai.v40i29.39628)] - DA-DFGAS: Differentiable Federated Graph Neural Architecture Search with Distribution-Aware Attentive Aggregation. [[PUB](https://doi.org/10.1609/aaai.v40i28.39573)] - Data Heterogeneity and Forgotten Labels in Split Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i31.39794)] - Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data. [[PUB](https://doi.org/10.1609/aaai.v40i29.39660)] - Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation. [[PUB](https://doi.org/10.1609/aaai.v40i34.40109)] - DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew. [[PUB](https://doi.org/10.1609/aaai.v40i30.39778)] - Domain-Aware Suppression and Aggregation for Federated DG ReID. [[PUB](https://doi.org/10.1609/aaai.v40i14.38214)] - DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models. [[PUB](https://doi.org/10.1609/aaai.v40i15.38239)] - Enhanced Federated Deep Multi-View Clustering Under Uncertainty Scenario. [[PUB](https://doi.org/10.1609/aaai.v40i32.39891)] - Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection. [[PUB](https://doi.org/10.1609/aaai.v40i35.40206)] - EvoFMVC: Trusted Federated Multi-View Clustering with Evolutionary Fusion. [[PUB](https://doi.org/10.1609/aaai.v40i33.40057)] - Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences. [[PUB](https://doi.org/10.1609/aaai.v40i28.39557)] - FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models. [[PUB](https://doi.org/10.1609/aaai.v40i28.39549)] - FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA. [[PUB](https://doi.org/10.1609/aaai.v40i24.39054)] - FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification. [[PUB](https://doi.org/10.1609/aaai.v40i14.38124)] - FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training. [[PUB](https://doi.org/10.1609/aaai.v40i28.39500)] - FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning. [[PUB](https://doi.org/10.1609/aaai.v40i34.40083)] - FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i28.39494)] - FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data. [[PUB](https://doi.org/10.1609/aaai.v40i25.39176)] - FedDNA: DNA Sequence Reconstruction via Deep Evidential Learning and Personalized Federated Aggregation. [[PUB](https://doi.org/10.1609/aaai.v40i28.39524)] - Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification. [[PUB](https://doi.org/10.1609/aaai.v40i32.39912)] - Federated Context-Aware Personalized Recommendation. [[PUB](https://doi.org/10.1609/aaai.v40i31.39888)] - Federated Graph-level Clustering Network with Attribute Inference. [[PUB](https://doi.org/10.1609/aaai.v40i26.39307)] - Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint. [[PUB](https://doi.org/10.1609/aaai.v40i25.39251)] - Federated Learning Playground. [[PUB](https://doi.org/10.1609/aaai.v40i48.42349)] - Federated Linear Dueling Bandits. [[PUB](https://doi.org/10.1609/aaai.v40i26.39361)] - Federated Vision-Language-Recommendation with Personalized Fusion. [[PUB](https://doi.org/10.1609/aaai.v40i28.39503)] - FedLAGC: Towards High Performance System-Heterogeneous Federated Learning via Layer-Adaptive Submodel Extraction and Gradient Correction. [[PUB](https://doi.org/10.1609/aaai.v40i26.39338)] - FedMerge: Federated Model Merging for Personalization. [[PUB](https://doi.org/10.1609/aaai.v40i24.39113)] - FedPKDA: Personalized Federated Learning with Privacy-Preserving Knowledge Dynamic Alignment. [[PUB](https://doi.org/10.1609/aaai.v40i33.40037)] - FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. [[PUB](https://doi.org/10.1609/aaai.v40i26.39368)] - FedP²EFT: Federated Learning to Personalize PEFT for Multilingual LLMs. [[PUB](https://doi.org/10.1609/aaai.v40i27.39443)] - FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning. [[PUB](https://doi.org/10.1609/aaai.v40i26.39359)] - FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification. [[PUB](https://doi.org/10.1609/aaai.v40i12.37918)] - FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD. [[PUB](https://doi.org/10.1609/aaai.v40i26.39364)] - FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i34.40100)] - FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness. [[PUB](https://doi.org/10.1609/aaai.v40i32.39895)] - FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting. [[PUB](https://doi.org/10.1609/aaai.v40i25.39210)] - FedTopo: Topology-Informed Representation Alignment in Federated Learning Under Non-I.I.D. Conditions. [[PUB](https://doi.org/10.1609/aaai.v40i26.39337)] - FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40859)] - Generalizable Heterogeneity-aware Federated Feature and Basic-matrix Consistency Learning. [[PUB](https://doi.org/10.1609/aaai.v40i27.39436)] - Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40878)] - Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation. [[PUB](https://doi.org/10.1609/aaai.v40i16.38328)] - HealSplit: Towards Self-Healing Through Adversarial Distillation in Split Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40908)] - Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. [[PUB](https://doi.org/10.1609/aaai.v40i24.39116)] - Incomplete Multi-View Unsupervised Federated Feature Selection via Cooperative Particle Swarm Optimization and Tensor-Aligned Learning. [[PUB](https://doi.org/10.1609/aaai.v40i33.40005)] - Inter-Client Dependency Recovery with Hidden Global Components for Federated Traffic Prediction. [[PUB](https://doi.org/10.1609/aaai.v40i34.40130)] - Intra-Class Unbiased Prototype Aggregation and Classifier Collaboration for Personalized Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i34.40113)] - Investigating Social Bias Propagation in Federated Fine-tuning of Large Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i46.41316)] - LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping. [[PUB](https://doi.org/10.1609/aaai.v40i25.39184)] - MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift. [[PUB](https://doi.org/10.1609/aaai.v40i32.39952)] - Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization. [[PUB](https://doi.org/10.1609/aaai.v40i25.39177)] - MultiKD: Backdoor Defense in Federated Graph Learning via Attention-Guided Multi-Teacher Distillation. [[PUB](https://doi.org/10.1609/aaai.v40i33.40051)] - Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling. [[PUB](https://doi.org/10.1609/aaai.v40i29.39624)] - Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i33.40045)] - Optimal Look-back Horizon for Time Series Forecasting in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i30.39781)] - OPTION: An Online Pricing Strategy for Asynchronous Federated Learning Against Free-Riding Attacks. [[PUB](https://doi.org/10.1609/aaai.v40i29.39653)] - OursFed: Provable Group Fairness-Aware Federated Learning Against Distrust and Fragility. [[PUB](https://doi.org/10.1609/aaai.v40i32.39926)] - PAGE: A Unified Approach for Federated Graph Unlearning. [[PUB](https://doi.org/10.1609/aaai.v40i24.39038)] - Personalized Federated Graph-Level Clustering Network. [[PUB](https://doi.org/10.1609/aaai.v40i28.39546)] - Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching. [[PUB](https://doi.org/10.1609/aaai.v40i25.39185)] - Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation. [[PUB](https://doi.org/10.1609/aaai.v40i19.38660)] - Poisoning with a Pill: Circumventing Detection in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i26.39290)] - PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i24.39073)] - Prior Refinement Is Better: Diffusion-Driven Graph Harmonization for Federated Graph Learning. [[PUB](https://doi.org/10.1609/aaai.v40i34.40163)] - Re-architecting Personalized Federated Learning for Demanding Edge Environments. [[PUB](https://doi.org/10.1609/aaai.v40i29.39655)] - REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature. [[PUB](https://doi.org/10.1609/aaai.v40i32.39911)] - Retaliatory Attacks Against Federated Unlearning via Data Leakage. [[PUB](https://doi.org/10.1609/aaai.v40i30.39725)] - Ripple Shapley: Data Influence Attribution in One Federated Training Run. [[PUB](https://doi.org/10.1609/aaai.v40i33.40034)] - Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?. [[PUB](https://doi.org/10.1609/aaai.v40i24.39122)] - SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i31.39787)] - ShadeEdit: A Utility-Preserving and Defense-Evasive Knowledge Manipulation Attack in Federated LLMs. [[PUB](https://doi.org/10.1609/aaai.v40i41.40787)] - SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data. [[PUB](https://doi.org/10.1609/aaai.v40i32.39977)] - Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack. [[PUB](https://doi.org/10.1609/aaai.v40i32.39979)] - TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i33.40058)] - Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy. [[PUB](https://doi.org/10.1609/aaai.v40i28.39582)] - Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework. [[PUB](https://doi.org/10.1609/aaai.v40i26.39311)] - Towards Robust Text-Attributed Federated Graph Learning: Multimodal Threats and Defense. [[PUB](https://doi.org/10.1609/aaai.v40i30.39732)] - TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i33.40048)] - Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic Flow Forecasting. [[PUB](https://doi.org/10.1609/aaai.v40i2.37083)] - Venom: Liquid Diffusion-Guided Gradient Inversion for Breaking Differential Privacy in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i26.39333)] - AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract). [[PUB](https://doi.org/10.1609/aaai.v40i48.42197)] - Federated Cross-Modal Style-Aware Prompt Generation (Student Abstract). [[PUB](https://doi.org/10.1609/aaai.v40i48.42268)] - UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data (Student Abstract). [[PUB](https://doi.org/10.1609/aaai.v40i48.42220)] - A Dialogue-Based Learning Analytics Framework for Collaborative Game-Based Learning. [[PUB](https://doi.org/10.1609/aaai.v40i48.42116)] - Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization. [[PUB](https://doi.org/10.1609/aaai.v40i2.37142)] - CL-Guard: Defending DNNs Against Backdoors via Fine-Grained Neuron Analysis and Collaborative Dual-Network Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40904)] - Collaborative Dual Representations for Semi-Supervised Partial Label Learning. [[PUB](https://doi.org/10.1609/aaai.v40i24.39049)] - Collaborative Feature Matching with Progressive Correspondence Learning. [[PUB](https://doi.org/10.1609/aaai.v40i9.37669)] - Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities. [[PUB](https://doi.org/10.1609/aaai.v40i22.38956)] - Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i24.39086)] - DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning. [[PUB](https://doi.org/10.1609/aaai.v40i28.39561)] - Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i37.40433)] - Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning. [[PUB](https://doi.org/10.1609/aaai.v40i19.38656)] - From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i32.39902)] - GLOBA: Rethinking Parameter Conflicts in Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i28.39572)] - Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation. [[PUB](https://doi.org/10.1609/aaai.v40i30.39742)] - Learning to Deliberate: Meta-policy Collaboration for Agentic LLMs with Multi-agent Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v40i35.40228)] - Learning to Generate and Extract: A Multi-Agent Collaboration Framework for Zero-Shot Document-Level Event Arguments Extraction. [[PUB](https://doi.org/10.1609/aaai.v40i41.40767)] - LLM Collaboration with Multi-Agent Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v40i38.40487)] - M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data. [[PUB](https://doi.org/10.1609/aaai.v40i31.39854)] - MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation. [[PUB](https://doi.org/10.1609/aaai.v40i14.38161)] - MergeDNA: Context-Aware Genome Modeling with Dynamic Tokenization Through Token Merging. [[PUB](https://doi.org/10.1609/aaai.v40i1.37032)] - Multi-view Invariance Learning for 3D Scene Graph Pre-training via Collaborative Cross-Modal Regularization. [[PUB](https://doi.org/10.1609/aaai.v40i7.37435)] - Outlier Matters: Efficient Long-to-Short Reasoning via Outlier-Guided Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i41.40828)] - RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior. [[PUB](https://doi.org/10.1609/aaai.v40i40.40722)] - Rep Deep &amp; Machine Learning: Exemplar-Free Continual Video Action Recognition via Slow-Fast Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40924)] - Revisiting Contrastive Learning in Collaborative Filtering via Parallel Graph Filters. [[PUB](https://doi.org/10.1609/aaai.v40i17.38521)] - Think Wise, Collaborate Effectively: A Rationale-Aware LLM-Based Recommender with Reinforcement Learning from Collaborative Signals. [[PUB](https://doi.org/10.1609/aaai.v40i18.38590)] - Unifying Multi-View Knowledge for Graph Learning via Model Collaboration. [[PUB](https://doi.org/10.1609/aaai.v40i32.39914)] - Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent (Abstract Reprint). [[PUB](https://doi.org/10.1609/aaai.v40i47.41386)] #### AI - Disentangling data distribution for optimal and communication-efficient federated learning. [[PUB](https://doi.org/10.1016/j.artint.2025.104455)] - Federated neural nonparametric point processes. [[PUB](https://doi.org/10.1016/j.artint.2025.104454)] ### 2025 #### IJCAI - Exploiting Label Skewness for Spiking Neural Networks in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/767)] - FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients. [[PUB](https://www.ijcai.org/proceedings/2025/379)] - Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts. [[PUB](https://www.ijcai.org/proceedings/2025/610)] - Pixel-wise Divide and Conquer for Federated Vessel Segmentation. [[PUB](https://www.ijcai.org/proceedings/2025/540)] - Universal Backdoor Defense via Label Consistency in Vertical Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/528)] - Where Does This Data Come From? Enhanced Source Inference Attacks in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/536)] - Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning. [[PUB](https://www.ijcai.org/proceedings/2025/541)] [[CODE](https://github.com/lancasterJie/FLAYER)] - FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks. [[PUB](https://www.ijcai.org/proceedings/2025/559)] [[CODE](https://github.com/dingbinb/FedDLAD)] - Federated Multi-view Graph Clustering with Incomplete Attribute Imputation. [[PUB](https://www.ijcai.org/proceedings/2025/570)] - ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network. [[PUB](https://www.ijcai.org/proceedings/2025/585)] - Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning. [[PUB](https://www.ijcai.org/proceedings/2025/590)] - FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition. [[PUB](https://www.ijcai.org/proceedings/2025/597)] - FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data. [[PUB](https://www.ijcai.org/proceedings/2025/602)] - FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity. [[PUB](https://www.ijcai.org/proceedings/2025/333)] - FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [[PUB](https://www.ijcai.org/proceedings/2025/612)] - Data Poisoning Attack Defense and Evolutionary Domain Adaptation for Federated Medical Image Segmentation. [[PUB](https://www.ijcai.org/proceedings/2025/146)] - Distilling A Universal Expert from Clustered Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/620)] - CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [[PUB](https://www.ijcai.org/proceedings/2025/621)] - FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/628)] - Hypernetwork Aggregation for Decentralized Personalized Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/161)] - Federated Domain Generalization with Decision Insight Matrix. [[PUB](https://www.ijcai.org/proceedings/2025/633)] - Generic Adversarial Attack Framework Against Vertical Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/646)] - One-shot Federated Learning Methods: A Practical Guide. [[PUB](https://www.ijcai.org/proceedings/2025/1174)] - Federated Learning at the Forefront of Fairness: A Multifaceted Perspective. [[PUB](https://www.ijcai.org/proceedings/2025/1177)] - Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach. [[PUB](https://www.ijcai.org/proceedings/2025/670)] - Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization. [[PUB](https://www.ijcai.org/proceedings/2025/677)] - FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data. [[PUB](https://www.ijcai.org/proceedings/2025/692)] [[CODE](https://github.com/Yuxia-Sun/FL_FedAPA)] - An Empirical Study of Federated Prompt Learning for Vision Language Model. [[PUB](https://www.ijcai.org/proceedings/2025/1188)] - FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation. [[PUB](https://www.ijcai.org/proceedings/2025/706)] - Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric. [[PUB](https://www.ijcai.org/proceedings/2025/733)] - DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning Under Two-sided Incomplete Information. [[PUB](https://www.ijcai.org/proceedings/2025/744)] - Backdoor Attack on Vertical Federated Graph Neural Network Learning. [[PUB](https://www.ijcai.org/proceedings/2025/877)] - Federated Low-Rank Adaptation for Foundation Models: A Survey. [[PUB](https://www.ijcai.org/proceedings/2025/1196)] - Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/761)] - FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization. [[PUB](https://www.ijcai.org/proceedings/2025/770)] - A Multi-Granularity Clustering Approach for Federated Backdoor Defense with the Adam Optimizer. [[PUB](https://www.ijcai.org/proceedings/2025/771)] - Federated Stochastic Bilevel Optimization with Fully First-Order Gradients. [[PUB](https://www.ijcai.org/proceedings/2025/784)] - AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling. [[PUB](https://www.ijcai.org/proceedings/2025/787)] - Rethinking Federated Graph Learning: A Data Condensation Perspective. [[PUB](https://www.ijcai.org/proceedings/2025/775)] - MMGIA: Gradient Inversion Attack Against Multimodal Federated Learning via Intermodal Correlation. [[PUB](https://www.ijcai.org/proceedings/2025/886)] - Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/798)] - Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning. [[PUB](https://www.ijcai.org/proceedings/2025/808)] - Inconsistency-Based Federated Active Learning. [[PUB](https://www.ijcai.org/proceedings/2025/812)] - CSAHFL: Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [[PUB](https://doi.org/10.24963/ijcai.2025/621)] - FedCPD: Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [[PUB](https://doi.org/10.24963/ijcai.2025/612)] - Bidirectional Human-AI Collaboration for Equitable Student Performance Prediction via Deep Uncertainty Learning. [[PUB](https://doi.org/10.24963/ijcai.2025/1114)] - Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing. [[PUB](https://doi.org/10.24963/ijcai.2025/459)] - Cross-modal Collaborative Representation Learning for Text-to-Image Person Retrieval. [[PUB](https://doi.org/10.24963/ijcai.2025/240)] - Enhancing Mixture of Experts with Independent and Collaborative Learning for Long-Tail Visual Recognition. [[PUB](https://doi.org/10.24963/ijcai.2025/93)] [[CODE](https://github.com/PolarisLight/ICL)] #### AISTATS - Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. [[PUB](https://proceedings.mlr.press/v258/thakur25a.html)] [[CODE](https://github.com/AnshThakur/FedMode)] - FedBaF: Federated Learning Aggregation Biased by a Foundation Model. [[PUB](https://proceedings.mlr.press/v258/park25b.html)] - Global Group Fairness in Federated Learning via Function Tracking. [[PUB](https://proceedings.mlr.press/v258/rychener25a.html)] [[CODE](https://github.com/yvesrychener/Fair-FL)] - On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond. [[PUB](https://proceedings.mlr.press/v258/zeng25b.html)] [[CODE](https://github.com/dunzeng/FedAWARE)] - Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. [[PUB](https://proceedings.mlr.press/v258/labbi25a.html)] [[CODE](https://github.com/Labbi-Safwan/Fed-UCBVI)] - ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. [[PUB](https://proceedings.mlr.press/v258/ozkara25a.html)] [[CODE](https://github.com/kazkara/adept)] - Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. [[PUB](https://proceedings.mlr.press/v258/khellaf25a.html)] [[CODE](https://github.com/RemiKhellaf/FedCausal-RCTs-Khellaf/)] - The cost of local and global fairness in Federated Learning. [[PUB](https://proceedings.mlr.press/v258/duan25a.html)] [[CODE](https://github.com/papersubmission678/The-cost-of-local-and-global-fairness-in-FL)] - Federated Communication-Efficient Multi-Objective Optimization. [[PUB](https://proceedings.mlr.press/v258/askin25a.html)] [[CODE](https://github.com/askinb/FedCMOO)] - Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. [[PUB](https://proceedings.mlr.press/v258/mangold25a.html)] [[CODE](https://pmangold.fr/papers/fed-richardson-romberg/supplementary.zip)] - Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning. [[PUB](https://proceedings.mlr.press/v258/zhang25l.html)] [[CODE](https://github.com/Bernie0115/LR-BPFL)] - On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients. [[PUB](https://proceedings.mlr.press/v258/keshri25a.html)] [[CODE](https://github.com/SatishKeshri/Continual_FL)] - DPFL: Decentralized Personalized Federated Learning. [[PUB](https://proceedings.mlr.press/v258/kharrat25a.html)] [[CODE](https://github.com/salmakh1/DPFL)] - Unbiased Quantization of the L1 Ball for Communication-Efficient Distributed Mean Estimation. [[PUB](https://proceedings.mlr.press/v258/babu25a.html)] #### AI - FedHM: Efficient federated learning for heterogeneous models via low-rank factorization. [[PUB](https://www.sciencedirect.com/science/article/pii/S0004370225000529)] #### AAAI - Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34475)] - Single-Loop Federated Actor-Critic across Heterogeneous Environments. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34469)] - Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34468)] - FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34464)] - FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34459)] - ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34449)] - Personalized Label Inference Attack in Federated Transfer Learning via Contrastive Meta Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34438)] - Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33455)] - Asynchronous Federated Clustering with Unknown Number of Clusters. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34429)] - Generating Synthetic Data for Unsupervised Federated Learning of Cross-Modal Retrieval. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34415)] - HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34409)] - LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34404)] - Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33440)] - Modeling Inter-Intra Heterogeneity for Graph Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34378)] - pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34368)] - First-Order Federated Bilevel Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34355)] - GAS: Generative Activation-Aided Asynchronous Split Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35503)] - FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35497)] - Federated Graph Condensation with Information Bottleneck Principles. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33417)] - A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35318)] - Federated Learning with Sample-level Client Drift Mitigation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35480)] - Pilot: Building the Federated Multimodal Instruction Tuning Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35476)] - Flexible Sharpness-Aware Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35475)] - MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32076)] - PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35460)] - Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35458)] - Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35444)] - Federated Recommendation with Explicitly Encoding Item Bias. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33395)] - Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34733)] - Decentralized Federated Learning with Model Caching on Mobile Agents. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35429)] - Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35426)] - FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35423)] - Reinforcement Active Client Selection for Federated Heterogeneous Graph Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35409)] - Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35405)] - Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35398)] - Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32807)] - Reputation-aware Revenue Allocation for Auction-based Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34296)] - Learn How to Query from Unlabeled Data Streams in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34287)] - Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35304)] - Graph Consistency and Diversity Measurement for Federated Multi-View Clustering. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34277)] - WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34272)] - Label-Free Backdoor Attacks in Vertical Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34246)] - Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35054)] - FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34228)] - Fair Federated Survival Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34214)] - Federated t-SNE and UMAP for Distributed Data Visualization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34204)] - Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34201)] - Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34197)] - In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34192)] - Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34187)] - Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33572)] - Federated Unlearning with Gradient Descent and Conflict Mitigation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34181)] - Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32671)] - FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34172)] - FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34129)] - Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34126)] - FedCross: Intertemporal Federated Learning Under Evolutionary Games. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34104)] - Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34094)] - SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34090)] - Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33328)] - Federated Graph-Level Clustering Network. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34077)] - LiD-FL: Towards List-Decodable Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34072)] - Convergence Analysis of Federated Learning Methods Using Backward Error Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34060)] - Progressive Distribution Matching for Federated Semi-Supervised Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32551)] - TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34053)] - Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34047)] - EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34046)] - FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34007)] - pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33980)] - FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33975)] - FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33942)] - Federated Learning with Heterogeneous LLMs: Integrating Small Student Client Models with a Large Hungry Model. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35332)] - PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33912)] - TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33524)] - FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33878)] - DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33877)] - Federated Assemblies. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33520)] - Federated Causally Invariant Feature Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33866)] [[CODE](https://github.com/Xianjie-Guo/FedCIFL)] - A New Federated Learning Framework Against Gradient Inversion Attacks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33865)] - Exploring Vacant Classes in Label-Skewed Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33864)] - Capture Global Feature Statistics for One-Shot Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33862)] - Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33839)] - MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32313)] - Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33830)] - Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33829)] - Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33822)] - SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33820)] - Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33814)] - How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33788)] - Attribute Inference Attacks for Federated Regression Tasks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33787)] - Federated Binary Matrix Factorization Using Proximal Optimization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33773)] - Creating Coherence in Federated Non-Negative Matrix Factorization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33772)] - Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33764)] - DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33746)] - Federated Foundation Models on Heterogeneous Time Series. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33739)] - FedPop: Federated Population-based Hyperparameter Tuning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33732)] - Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35005)] - EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33700)] - Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33678)] - Breaking Data Silos in Parkinson&apos;s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [[PUB](https://doi.org/10.1609/aaai.v39i13.33572)] - FedCFA: Alleviating Simpson&apos;s Paradox in Model Aggregation with Counterfactual Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v39i17.33942)] - Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection. [[PUB](https://doi.org/10.1609/aaai.v39i1.32109)] - Collaborative Learning for 3D Hand-Object Reconstruction and Compositional Action Recognition from Egocentric RGB Videos Using Superquadrics. [[PUB](https://doi.org/10.1609/aaai.v39i7.32800)] - DSRC: Learning Density-Insensitive and Semantic-Aware Collaborative Representation Against Corruptions. [[PUB](https://doi.org/10.1609/aaai.v39i9.33078)] - Learning to Collaborate with Unknown Agents in the Absence of Reward. [[PUB](https://doi.org/10.1609/aaai.v39i13.33589)] - MergeNet: Knowledge Migration Across Heterogeneous Models, Tasks, and Modalities. [[PUB](https://doi.org/10.1609/aaai.v39i5.32510)] - Multi-concept Model Immunization through Differentiable Model Merging. [[PUB](https://doi.org/10.1609/aaai.v39i10.33145)] - Multi-View Collaborative Learning Network for Speech Deepfake Detection. [[PUB](https://doi.org/10.1609/aaai.v39i1.32094)] - Multimodal Promptable Token Merging for Diffusion Models. [[PUB](https://doi.org/10.1609/aaai.v39i16.33894)] - Paid with Models: Optimal Contract Design for Collaborative Machine Learning. [[PUB](https://doi.org/10.1609/aaai.v39i13.33552)] - The Dynamic Duo of Collaborative Masking and Target for Advanced Masked Autoencoder Learning. [[PUB](https://doi.org/10.1609/aaai.v39i18.34145)] - Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v39i16.33813)] ### 2024 #### alt - Optimal Regret Bounds for Collaborative Learning in Bandits. [[PUB](https://proceedings.mlr.press/v237/shidani24a.html)] #### IJCAI - Federated Multi-View Clustering via Tensor Factorization. [[PUB](https://www.ijcai.org/proceedings/2024/438)] - Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means. [[PUB](https://www.ijcai.org/proceedings/2024/439)] - LG-FGAD: An Effective Federated Graph Anomaly Detection Framework. [[PUB](https://www.ijcai.org/proceedings/2024/416)] - Federated Prompt Learning for Weather Foundation Models on Devices. [[PUB](https://www.ijcai.org/proceedings/2024/638)] - Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation. [[PUB](https://www.ijcai.org/proceedings/2024/419)] - Unlearning during Learning: An Efficient Federated Machine Unlearning Method. [[PUB](https://www.ijcai.org/proceedings/2024/446)] - Practical Hybrid Gradient Compression for Federated Learning Systems. [[PUB](https://www.ijcai.org/proceedings/2024/458)] - Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection. [[PUB](https://www.ijcai.org/proceedings/2024/450)] [[CODE](https://github.com/Xianjie-Guo/FedACD)] - Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains. [[PUB](https://www.ijcai.org/proceedings/2024/457)] [[CODE](https://github.com/LonelyMoonDesert/FNR-FL)] - Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. [[PUB](https://www.ijcai.org/proceedings/2024/788)] - FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients. [[PUB](https://www.ijcai.org/proceedings/2024/501)] - DarkFed: A Data-Free Backdoor Attack in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/491)] - Scalable Federated Unlearning via Isolated and Coded Sharding. [[PUB](https://www.ijcai.org/proceedings/2024/503)] - Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning. [[PUB](https://www.ijcai.org/proceedings/2024/238)] - Label Leakage in Vertical Federated Learning: A Survey. [[PUB](https://www.ijcai.org/proceedings/2024/902)] - The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence. [[PUB](https://www.ijcai.org/proceedings/2024/980)] - LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game. [[PUB](https://www.ijcai.org/proceedings/2024/515)] - EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/51)] - Knowledge Distillation in Federated Learning: A Practical Guide. [[PUB](https://www.ijcai.org/proceedings/2024/905)] - FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization. [[PUB](https://www.ijcai.org/proceedings/2024/526)] - FedPFT: Federated Proxy Fine-Tuning of Foundation Models. [[PUB](https://www.ijcai.org/proceedings/2024/531)] [[CODE](https://github.com/pzp-dzd/FedPFT)] - A Systematic Survey on Federated Semi-supervised Learning. [[PUB](https://www.ijcai.org/proceedings/2024/911)] - Intelligent Agents for Auction-based Federated Learning: A Survey. [[PUB](https://www.ijcai.org/proceedings/2024/912)] - A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/552)] - Dual Calibration-based Personalised Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/551)] - Stakeholder-oriented Decision Support for Auction-based Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/972)] - Redefining Contributions: Shapley-Driven Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/554)] [[CODE](https://github.com/tnurbek/shapfed}{https://github.com/tnurbek/shapfed)] - A Survey on Efficient Federated Learning Methods for Foundation Model Training. [[PUB](https://www.ijcai.org/proceedings/2024/919)] - From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching. [[PUB](https://www.ijcai.org/proceedings/2024/575)] [[CODE](https://github.com/wnn2000/FFL4MIA)] - FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/585)] - FedFa: A Fully Asynchronous Training Paradigm for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/584)] - FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/594)] - FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise. [[PUB](https://www.ijcai.org/proceedings/2024/599)] - Personalized Federated Learning for Cross-City Traffic Prediction. [[PUB](https://www.ijcai.org/proceedings/2024/611)] - Federated Adaptation for Foundation Model-based Recommendations. [[PUB](https://www.ijcai.org/proceedings/2024/603)] - BADFSS: Backdoor Attacks on Federated Self-Supervised Learning. [[PUB](https://www.ijcai.org/proceedings/2024/61)] - Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning. [[PUB](https://www.ijcai.org/proceedings/2024/290)] [[CODE](https://github.com/GuogangZhu/FedDB)] - FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/632)] - Graph Collaborative Expert Finding with Contrastive Learning. [[PUB](https://www.ijcai.org/proceedings/2024/253)] #### AISTATS - BOBA: Byzantine-Robust Federated Learning with Label Skewness. [[PUB](https://proceedings.mlr.press/v238/bao24a.html)] [[PDF](https://arxiv.org/abs/2208.12932)] [[CODE](https://github.com/baowenxuan/BOBA)] - Federated Linear Contextual Bandits with Heterogeneous Clients. [[PUB](https://proceedings.mlr.press/v238/blaser24a.html)] [[PDF](https://arxiv.org/abs/2403.00116)] [[CODE](https://github.com/blaserethan/HetoFedBandit)] - Federated Experiment Design under Distributed Differential Privacy. [[PUB](https://proceedings.mlr.press/v238/chen24c.html)] [[PDF](https://arxiv.org/abs/2311.04375)] [[CODE](https://drive.google.com/file/d/1ugYQQEIOwqc1oH8cUe6rf1mV91c-cF_g/view?usp=drive_link)] - Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression. [[PUB](https://proceedings.mlr.press/v238/chen24d.html)] [[PDF](https://arxiv.org/abs/2310.19059)] - Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. [[PUB](https://proceedings.mlr.press/v238/even24a.html)] [[PDF](https://arxiv.org/abs/2311.00465)] - SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. [[PUB](https://proceedings.mlr.press/v238/fraboni24a.html)] [[PDF](https://arxiv.org/abs/2211.11656)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/SIFU)] - Compression with Exact Error Distribution for Federated Learning. [[PUB](https://proceedings.mlr.press/v238/hegazy24a.html)] [[PDF](https://arxiv.org/abs/2310.20682)] [[CODE](https://github.com/mahegz/CompWithExactError)] - Adaptive Federated Minimax Optimization with Lower Complexities. [[PUB](https://proceedings.mlr.press/v238/huang24c.html)] [[PDF](https://arxiv.org/abs/2211.07303)] - Adaptive Compression in Federated Learning via Side Information. [[PUB](https://proceedings.mlr.press/v238/isik24a.html)] [[PDF](https://arxiv.org/abs/2306.12625)] [[CODE](https://github.com/FrancescoPase/Federated-KLMS)] - On-Demand Federated Learning for Arbitrary Target Class Distributions. [[PUB](https://proceedings.mlr.press/v238/jeong24a.html)] [[CODE](https://github.com/eai-lab/On-DemandFL)] - FedFisher: Leveraging Fisher Information for One-Shot Federated Learning. [[PUB](https://proceedings.mlr.press/v238/jhunjhunwala24a.html)] [[PDF](https://arxiv.org/abs/2403.12329)] [[CODE](https://github.com/Divyansh03/FedFisher)] - Queuing dynamics of asynchronous Federated Learning. [[PUB](https://proceedings.mlr.press/v238/leconte24a.html)] [[PDF](https://arxiv.org/abs/2405.00017)] - Personalized Federated X-armed Bandit. [[PUB](https://proceedings.mlr.press/v238/li24a.html)] [[PDF](https://arxiv.org/abs/2310.16323)] [[CODE](https://github.com/WilliamLwj/PyXAB)] - Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. [[PUB](https://proceedings.mlr.press/v238/molaei24a.html)] [[CODE](https://github.com/AnshThakur/FL4HeterogenousEHRs)] - Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. [[PUB](https://proceedings.mlr.press/v238/shen24c.html)] [[PDF](https://arxiv.org/abs/2311.00944)] - Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters. [[PUB](https://proceedings.mlr.press/v238/sun24a.html)] [[PDF](https://arxiv.org/abs/2306.03824)] [[CODE](https://github.com/fedcodexx/Generalization-of-Federated-Learning)] - Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. [[PUB](https://proceedings.mlr.press/v238/tsoy24a.html)] [[PDF](https://arxiv.org/abs/2403.06672)] [[CODE](https://github.com/nikita-tsoy98/mutually-beneficial-federated-learning-replication)] - Analysis of Privacy Leakage in Federated Large Language Models. [[PUB](https://proceedings.mlr.press/v238/vu24a.html)] [[PDF](https://arxiv.org/abs/2403.04784)] [[CODE](https://github.com/vunhatminh/FL_Attacks.git)] - Invariant Aggregator for Defending against Federated Backdoor Attacks. [[PUB](https://proceedings.mlr.press/v238/wang24e.html)] [[PDF](https://arxiv.org/abs/2210.01834)] [[CODE](https://github.com/Xiaoyang-Wang/InvariantAggregator)] - Communication-Efficient Federated Learning With Data and Client Heterogeneity. [[PUB](https://proceedings.mlr.press/v238/zakerinia24a.html)] [[PDF](https://arxiv.org/abs/2206.10032)] [[CODE](https://github.com/ShayanTalaei/QuAFL)] #### AAAI - FedMut: Generalized Federated Learning via Stochastic Mutation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29146)] - Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29562)] [[PAGE](https://underline.io/lecture/93915-federated-partial-label-learning-with-local-adaptive-augmentation-and-regularization)] - No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28950)] [[PAGE](https://underline.io/lecture/93775-no-prejudice-fair-federated-graph-neural-networks-for-personalized-recommendation)] [[PDF](https://arxiv.org/abs/2312.10080)] [[CODE](https://github.com/nimeshagrawal/F2PGNN-AAAI24)] - Formal Logic Enabled Personalized Federated Learning through Property Inference. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28962)] [[PDF](https://arxiv.org/abs/2401.07448)] - Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28965)] [[PAGE](https://underline.io/lecture/91722-task-agnostic-privacy-preserving-representation-learning-for-federated-learning-against-attribute-inference-attacks)] [[PDF](https://arxiv.org/abs/2312.06989)] [[CODE](https://github.com/TAPPFL/TAPPFL)] - FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28971)] [[PAGE](https://underline.io/lecture/93537-fairtrade-achieving-pareto-optimal-trade-offs-between-balanced-accuracy-and-fairness-in-federated-learning)] - Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28974)] [[PAGE](https://underline.io/lecture/92397-combating-data-imbalances-in-federated-semi-supervised-learning-with-dual-regulators)] [[PDF](https://arxiv.org/abs/2307.05358)] - Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29025)] [[PAGE](https://underline.io/lecture/93417-fed-qssl-a-framework-for-personalized-federated-learning-under-bitwidth-and-data-heterogeneity)] [[PDF](https://arxiv.org/abs/2312.13380)] - On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29010)] - FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29007)] [[PAGE](https://underline.io/lecture/91710-feddat-an-approach-for-foundation-model-finetuning-in-multi-modal-heterogeneous-federated-learning)] [[PDF](https://arxiv.org/abs/2308.12305)] [[CODE](https://github.com/HaokunChen245/FedDAT)] - Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29012)] [[PAGE](https://underline.io/lecture/91776-watch-your-head-assembling-projection-heads-to-save-the-reliability-of-federated-models)] [[PDF](https://arxiv.org/abs/2402.16255)] - FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29031)] [[PAGE](https://underline.io/lecture/92275-fedgcr-achieving-performance-and-fairness-for-federated-learning-with-distinct-client-types-via-group-customization-and-reweighting)] [[CODE](https://github.com/celinezheng/fedgcr)] - Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27909)] [[PAGE](https://underline.io/lecture/91824-federated-modality-specific-encoders-and-multimodal-anchors-for-personalized-brain-tumor-segmentation)] [[PDF](https://arxiv.org/abs/2403.11803)] [[CODE](https://github.com/qdaiing/fedmema)] - Exploiting Label Skews in Federated Learning with Model Concatenation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29063)] [[PAGE](https://underline.io/lecture/92569-exploiting-label-skews-in-federated-learning-with-model-concatenation)] [[PDF](https://arxiv.org/abs/2312.06290)] [[CODE](https://github.com/sjtudyq/FedConcat)] - Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29958)] [[PAGE](https://underline.io/lecture/92937-complementary-knowledge-distillation-for-robust-and-privacy-preserving-model-serving-in-vertical-federated-learning)] - Federated Learning via Input-Output Collaborative Distillation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30209)] [[PAGE](https://underline.io/lecture/94089-federated-learning-via-input-output-collaborative-distillation)] [[PDF](https://arxiv.org/abs/2312.14478)] [[CODE](https://github.com/lsl001006/fediod)] - Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29122)] [[PAGE](https://underline.io/lecture/92727-calibrated-one-round-federated-learning-with-bayesian-inference-in-the-predictive-space)] [[PDF](https://arxiv.org/abs/2312.09817)] [[CODE](https://github.com/hasanmohsin/betaPredBayesFL)] - FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29113)] [[PDF](https://github.com/Xianjie-Guo/FedCSL)] [[CODE](https://github.com/Xianjie-Guo/FedCSL)] - FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29179)] [[PAGE](https://underline.io/lecture/92327-fedfixer-mitigating-heterogeneous-label-noise-in-federated-learning)] [[PDF](https://arxiv.org/abs/2403.16561)] - FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29181)] [[PAGE](https://underline.io/lecture/93122-fedlps-heterogeneous-federated-learning-for-multiple-tasks-with-local-parameter-sharing)] [[PDF](https://arxiv.org/abs/2402.08578)] [[CODE](https://github.com/jyzgh/FedLPS)] - Provably Convergent Federated Trilevel Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29190)] [[PDF](https://arxiv.org/abs/2312.11835)] - Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29191)] [[PAGE](https://underline.io/lecture/93963-performative-federated-learning-a-solution-to-model-dependent-and-heterogeneous-distribution-shifts)] - General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30309)] [[PAGE](https://underline.io/lecture/91475-general-commerce-intelligence-glocally-federated-nlp-based-engine-for-privacy-preserving-and-sustainable-personalized-services-of-multi-merchants)] - EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29258)] [[PAGE](https://underline.io/lecture/91709-emgan-early-mix-gan-on-extracting-server-side-model-in-split-federated-learning)] [[CODE](https://github.com/zlijingtao/SFL-MEA)] - FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28095)] [[PAGE](https://underline.io/lecture/91764-feddiv-collaborative-noise-filtering-for-federated-learning-with-noisy-labels)] [[PDF](https://arxiv.org/abs/2312.12263)] [[CODE](https://github.com/lijichang/FLNL-FedDiv)] - Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28082)] [[PAGE](https://underline.io/lecture/92706-point-transformer-with-federated-learning-for-predicting-breast-cancer-her2-status-from-hematoxylin-and-eosin-stained-whole-slide-images)] [[PDF](https://arxiv.org/abs/2312.06454)] [[CODE](https://github.com/Boyden/PointTransformerFL)] - FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29254)] [[PDF](https://arxiv.org/abs/2401.02734)] [[CODE](https://github.com/superlj666/fedns)] - Federated X-armed Bandit. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29267)] [[PAGE](https://underline.io/lecture/93049-federated-x-armed-bandit)] [[PDF](https://arxiv.org/abs/2205.15268)] [[CODE](https://github.com/williamlwj/pyxab)] - Algorithmic Foundation of Federated Learning with Sequential Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30291)] - UFDA: Universal Federated Domain Adaptation with Practical Assumptions. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29311)] [[PAGE](https://underline.io/lecture/93578-ufda-universal-federated-domain-adaptation-with-practical-assumptions)] [[PDF](https://arxiv.org/abs/2311.15570)] [[CODE](https://github.com/Xinhui-99/UFDA)] - FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29297)] [[PAGE](https://underline.io/lecture/92855-fedasmu-efficient-asynchronous-federated-learning-with-dynamic-staleness-aware-model-update)] [[PDF](https://arxiv.org/abs/2312.05770)] - Language-Guided Transformer for Federated Multi-Label Classification. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29295)] [[PAGE](https://underline.io/lecture/93447-language-guided-transformer-for-federated-multi-label-classification)] [[PDF](https://arxiv.org/abs/2312.07165)] [[CODE](https://github.com/Jack24658735/FedLGT)] - FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28175)] [[PAGE](https://underline.io/lecture/92166-fedcd-federated-semi-supervised-learning-with-class-awareness-balance-via-dual-teachers)] [[CODE](https://github.com/YunzZ-Liu/FedCD/)] - Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30131)] [[PAGE](https://underline.io/lecture/94230-beyond-traditional-threats-a-persistent-backdoor-attack-on-federated-learning)] [[CODE](https://github.com/PhD-TaoLiu/FCBA)] - Federated Learning with Extremely Noisy Clients via Negative Distillation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29329)] [[PAGE](https://underline.io/lecture/93309-federated-learning-with-extremely-noisy-clients-via-negative-distillation)] [[PDF](https://arxiv.org/abs/2312.12703)] [[CODE](https://github.com/linChen99/FedNed)] - FedST: Federated Style Transfer Learning for Non-IID Image Segmentation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28199)] [[PAGE](https://underline.io/lecture/93609-fedst-federated-style-transfer-learning-for-non-iid-image-segmentation)] [[学报](https://journal.bupt.edu.cn/CN/abstract/abstract5178.shtml)] [[CODE](https://github.com/YoferChen/FedST)] - PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29339)] [[PAGE](https://underline.io/lecture/92243-ppidsg-a-privacy-preserving-image-distribution-sharing-scheme-with-gan-in-federated-learning)] [[PDF](https://arxiv.org/abs/2312.10380)] [[CODE](https://github.com/ytingma/PPIDSG)] - A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30400)] - A Primal-Dual Algorithm for Hybrid Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29363)] [[PAGE](https://underline.io/lecture/93144-a-primal-dual-algorithm-for-hybrid-federated-learning)] [[PDF](https://arxiv.org/abs/2210.08106)] - FedLF: Layer-Wise Fair Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29368)] [[PAGE](https://underline.io/lecture/93087-fedlf-layer-wise-fair-federated-learning)] [[CODE](https://github.com/zibinpan/FedLF)] - Towards Fair Graph Federated Learning via Incentive Mechanisms. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29365)] [[PAGE](https://underline.io/lecture/92583-towards-fair-graph-federated-learning-via-incentive-mechanisms)] [[PDF](https://arxiv.org/abs/2312.13306)] [[CODE](https://github.com/Chenglu0426/FairGraphFL)] - Towards the Robustness of Differentially Private Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29967)] [[PAGE](https://underline.io/lecture/92491-towards-the-robustness-of-differentially-private-federated-learning)] - Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29385)] [[PAGE](https://underline.io/lecture/94020-resisting-backdoor-attacks-in-federated-learning-via-bidirectional-elections-and-individual-perspective)] [[PDF](https://arxiv.org/abs/2309.16456)] [[CODE](https://github.com/zhenqincn/Snowball)] - Integer Is Enough: When Vertical Federated Learning Meets Rounding. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29388)] [[PAGE](https://underline.io/lecture/93362-integer-is-enough-when-vertical-federated-learning-meets-rounding)] - CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29416)] [[PAGE](https://underline.io/lecture/92441-clip-guided-federated-learning-on-heterogeneity-and-long-tailed-data)] [[PDF](https://arxiv.org/abs/2312.08648)] [[CODE](https://github.com/shijiangming1/CLIP2FL)] - Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29434)] [[PAGE](https://underline.io/lecture/92772-federated-adaptive-prompt-tuning-for-multi-domain-collaborative-learning)] [[PDF](https://arxiv.org/abs/2211.07864)] [[CODE](https://github.com/leondada/fedapt)] - Multi-Dimensional Fair Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29430)] [[PAGE](https://underline.io/lecture/92619-multi-dimensional-fair-federated-learning)] [[PDF](https://arxiv.org/abs/2312.05551)] - HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30317)] - On the Role of Server Momentum in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29439)] [[PDF](https://arxiv.org/abs/2312.12670)] - FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29446)] [[PAGE](https://underline.io/lecture/93158-fedcompetitors-harmonious-collaboration-in-federated-learning-with-competing-participants)] [[PDF](https://arxiv.org/abs/2312.11391)] - z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29454)] [[PAGE](https://underline.io/lecture/93975-z-signfedavg-a-unified-stochastic-sign-based-compression-for-federated-learning)] [[PDF](https://arxiv.org/abs/2302.02589)] - Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29467)] [[PAGE](https://underline.io/lecture/92405-data-disparity-and-temporal-unavailability-aware-asynchronous-federated-learning-for-predictive-maintenance-on-transportation-fleets)] - Federated Graph Learning under Domain Shift with Generalizable Prototypes. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29468)] [[PAGE](https://underline.io/lecture/92526-federated-graph-learning-under-domain-shift-with-generalizable-prototypes)] - TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29481)] [[PAGE](https://underline.io/lecture/91900-turbosvm-fl-boosting-federated-learning-through-svm-aggregation-for-lazy-clients)] [[PDF](https://arxiv.org/abs/2401.12012)] [[CODE](https://github.com/Kasneci-Lab/TurboSVM-FL)] - Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29510)] [[PDF](https://arxiv.org/abs/2401.10272)] [[CODE](https://github.com/weiyikang/FedGM)] - Concealing Sensitive Samples against Gradient Leakage in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30171)] [[PAGE](https://underline.io/lecture/94147-concealing-sensitive-samples-against-gradient-leakage-in-federated-learning)] [[PDF](https://arxiv.org/abs/2209.05724)] [[CODE](https://github.com/JingWu321/DCS-2)] - FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29525)] [[PAGE](https://underline.io/lecture/92926-feda3i-annotation-quality-aware-aggregation-for-federated-medical-image-segmentation-against-heterogeneous-annotation-noise)] [[PDF](https://arxiv.org/abs/2312.12838)] [[CODE](https://github.com/wnn2000/FedAAAI)] - Federated Causality Learning with Explainable Adaptive Optimization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29566)] [[PAGE](https://underline.io/lecture/93217-federated-causality-learning-with-explainable-adaptive-optimization)] [[PDF](https://arxiv.org/abs/2312.05540)] - Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30045)] [[PAGE](https://underline.io/lecture/93664-federated-contextual-cascading-bandits-with-asynchronous-communication-and-heterogeneous-users)] [[PDF](https://arxiv.org/abs/2402.16312)] - Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29568)] [[PDF](https://arxiv.org/abs/2305.04063)] - Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28468)] [[PAGE](https://underline.io/lecture/91850-diversity-authenticity-co-constrained-stylization-for-federated-domain-generalization-in-person-re-identification)] - PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29576)] [[PAGE](https://underline.io/lecture/92749-perfedrlnas-one-for-all-personalized-federated-neural-architecture-search)] - Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29603)] [[PAGE](https://underline.io/lecture/92183-efficient-asynchronous-federated-learning-with-prospective-momentum-aggregation-and-fine-grained-correction)] - Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27796)] - FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29617)] [[PAGE](https://underline.io/lecture/91976-fedtgp-trainable-global-prototypes-with-adaptive-margin-enhanced-contrastive-learning-for-data-and-model-heterogeneity-in-federated-learning)] [[PDF](https://arxiv.org/abs/2401.03230)] [[CODE](https://github.com/TsingZ0/FedTGP)] - LR-XFL: Logical Reasoning-Based Explainable Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30179)] [[PDF](https://arxiv.org/abs/2308.12681)] [[CODE](https://github.com/yanci87/lr-xfl)] - A Huber Loss Minimization Approach to Byzantine Robust Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30181)] [[PAGE](https://underline.io/lecture/94170-a-huber-loss-minimization-approach-to-byzantine-robust-federated-learning)] [[PDF](https://arxiv.org/abs/2308.12581)] - Knowledge-Aware Parameter Coaching for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29651)] [[PAGE](https://underline.io/lecture/92711-knowledge-aware-parameter-coaching-for-personalized-federated-learning)] - Federated Label-Noise Learning with Local Diversity Product Regularization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29659)] [[PAGE](https://underline.io/lecture/92697-federated-label-noise-learning-with-local-diversity-product-regularization)] [[SUPP](https://wanglab.sjtu.edu.cn/userfiles/files/Supp_FedLNL.pdf)] - Adapted Weighted Aggregation in Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30557)] - Knowledge Transfer via Compact Model in Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30498)] [[PAGE](https://underline.io/lecture/91519-knowledge-transfer-via-compact-model-in-federated-learning-student-abstract)] - PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30438)] [[PAGE](https://underline.io/lecture/91585-picsr-prototype-informed-cross-silo-router-for-federated-learning-student-abstract)] - Adapted Weighted Aggregation in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v38i21.30557)] - Collaborative Consortium of Foundation Models for Open-World Few-Shot Learning. [[PUB](https://doi.org/10.1609/aaai.v38i5.28275)] [[CODE](https://github.com/The-Shuai/CO3)] - Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. [[PUB](https://doi.org/10.1609/aaai.v38i20.30284)] - Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis. [[PUB](https://doi.org/10.1609/aaai.v38i3.27983)] - Communication Efficient Distributed Newton Method over Unreliable Networks. [[PUB](https://doi.org/10.1609/aaai.v38i14.29513)] - DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection. [[PUB](https://doi.org/10.1609/aaai.v38i4.28105)] [[CODE](https://github.com/Serenos/DI-V2X)] - Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective. [[PUB](https://doi.org/10.1609/aaai.v38i11.29140)] - Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing. [[PUB](https://doi.org/10.1609/aaai.v38i4.28089)] - High-Fidelity Gradient Inversion in Distributed Learning. [[PUB](https://doi.org/10.1609/aaai.v38i18.29975)] [[CODE](https://github.com/MiLab-HITSZ/2023YeHFGradInv)] - Learn How to See: Collaborative Embodied Learning for Object Detection and Camera Adjusting. [[PUB](https://doi.org/10.1609/aaai.v38i5.28281)] [[CODE](https://github.com/lydonShen/STF)] ### 2023 #### AI - Privacy-preserving graph convolution network for federated item recommendation. [[PUB](https://www.sciencedirect.com/science/article/abs/pii/S000437022300142X)] - Transfer learning for collaborative recommendation with biased and unbiased data. [[PUB](https://doi.org/10.1016/j.artint.2023.103992)] #### AAAI - Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25531)] - Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25611)] [[PDF](https://arxiv.org/abs/2212.05399)] [[CODE](https://github.com/yflyl613/fedrec)] - Incentive-Boosted Federated Crowdsourcing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25744)] [[PDF](https://arxiv.org/abs/2211.14439)] - Tackling Data Heterogeneity in Federated Learning with Class Prototypes. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25891)] [[PDF](https://arxiv.org/abs/2212.02758)] [[CODE](https://github.com/yutong-dai/fednh)] - FairFed: Enabling Group Fairness in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25911)] [[PDF](https://arxiv.org/abs/2110.00857)] [[解读](https://zhuanlan.zhihu.com/p/613201113)] - Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25955)] [[CODE](https://github.com/illidanlab/FedRBN)] - Complement Sparsification: Low-Overhead Model Pruning for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25977)] - Almost Cost-Free Communication in Federated Best Arm Identification. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26010)] [[PDF](https://arxiv.org/abs/2208.09215)] - Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26023)] [[PDF](https://arxiv.org/abs/2110.10302)] - Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26083)] - FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26122)] [[CODE](https://github.com/zibinpan/FedMDFG)] - Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26177)] [[PDF](https://arxiv.org/abs/2106.03328)] [[VIDEO](https://slideslive.com/38960185/securing-secure-aggregation-mitigating-multiround-privacy-leakage-in-federated-learning)] [[CODE](https://openreview.net/attachment?id=nVV6S2sb_UL&name=supplementary_material)] - Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26187)] [[PDF](https://arxiv.org/abs/2211.13009)] [[CODE](https://github.com/yuetan031/fedstar)] - Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26197)] [[PDF](https://arxiv.org/abs/2209.10526)] [[CODE](https://github.com/mmorafah/pacfl)] - FedABC: Targeting Fair Competition in Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26203)] [[PDF](https://arxiv.org/abs/2302.07450)] - Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26212)] [[PDF](https://arxiv.org/abs/2212.01519)] - FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26223)] [[PDF](https://arxiv.org/abs/2211.13975)] [[CODE](https://github.com/wwzzz/fedgs)] - Faster Adaptive Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26235)] [[PDF](https://arxiv.org/abs/2212.00974)] - FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26237)] [[CODE](https://github.com/CodePothunter/fednp)] [[VIDEO](https://www.youtube.com/watch?v=3XM_NNvXCBo)] [[SUPP](https://github.com/CodePothunter/fednp/blob/main/appendix.pdf)] - Bayesian Federated Neural Matching That Completes Full Information. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26245)] [[PDF](https://arxiv.org/abs/2211.08010)] - CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26246)] [[PDF](https://arxiv.org/abs/2105.14216)] [[CODE](https://github.com/xjiajiahao/federated-minimax)] - Federated Generative Model on Multi-Source Heterogeneous Data in IoT. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26252)] - DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26271)] - FedALA: Adaptive Local Aggregation for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26330)] [[PDF](https://arxiv.org/abs/2212.01197)] [[CODE](https://github.com/tsingz0/fedala)] - Delving into the Adversarial Robustness of Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26331)] [[PDF](https://arxiv.org/abs/2302.09479)] - On the Vulnerability of Backdoor Defenses for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26393)] [[PDF](https://arxiv.org/abs/2301.08170)] [[CODE](https://github.com/jinghuichen/focused-flip-federated-backdoor-attack)] - Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26400)] [[PDF](https://arxiv.org/abs/2304.05516)] - Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [[PDF](https://arxiv.org/abs/2211.13009)] [[CODE](https://github.com/yuetan031/fedstar)] - FedGS: Federated Graph-based Sampling with Arbitrary Client Availability. [[PDF](https://arxiv.org/abs/2211.13975)] [[CODE](https://github.com/wwzzz/fedgs)] - Incentive-boosted Federated Crowdsourcing. [[PDF](https://arxiv.org/abs/2211.14439)] - DPAUC: Differentially Private AUC Computation in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26770)] [[CODE](https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC)] - Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26836)] - Industry-Scale Orchestrated Federated Learning for Drug Discovery. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26847)] - A Crowd-AI Collaborative Duo Relational Graph Learning Framework towards Social Impact Aware Photo Classification. [[PUB](https://doi.org/10.1609/aaai.v37i12.26711)] - CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v37i13.26893)] - Heterogeneous-Branch Collaborative Learning for Dialogue Generation. [[PUB](https://doi.org/10.1609/aaai.v37i11.26544)] - A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26984)] - Clustered Federated Learning for Heterogeneous Data (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27049)] - MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26995)] #### AAAI Special Tracks - DPAUC: Differentially Private AUC Computation in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26770)] [[PDF](https://arxiv.org/abs/2208.12294)] [[CODE](https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC)] #### AAAI Special Programs - Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26836)] [[PDF](https://arxiv.org/abs/2302.11485)] - Industry-Scale Orchestrated Federated Learning for Drug Discovery. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26847)] [[PDF](https://arxiv.org/abs/2210.08871)] [[VIDEO](https://www.youtube.com/watch?v=J_RmZhKzBcA)] - A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26984)] - MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26995)] - Clustered Federated Learning for Heterogeneous Data (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27049)] #### IJCAI - FedSampling: A Better Sampling Strategy for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/462)] [[PDF](https://arxiv.org/abs/2306.14245)] [[CODE](https://github.com/taoqi98/FedSampling)] - HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/440)] [[PDF](https://arxiv.org/abs/2307.14384)] - FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/394)] [[PDF](https://arxiv.org/abs/2208.05174)] [[CODE](https://github.com/cyyever/distributed_learning_simulator)] - Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation. [[PUB](https://www.ijcai.org/proceedings/2023/245)] - Federated Graph Semantic and Structural Learning. [[PUB](https://www.ijcai.org/proceedings/2023/426)] - BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/498)] [[PDF](https://arxiv.org/abs/2305.05221)] - FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment. [[PUB](https://www.ijcai.org/proceedings/2023/444)] [[PDF](https://arxiv.org/abs/2305.06124)] - FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation. [[PUB](https://www.ijcai.org/proceedings/2023/418)] [[PDF](https://arxiv.org/abs/2301.12623)] - Globally Consistent Federated Graph Autoencoder for Non-IID Graphs. [[PUB](https://www.ijcai.org/proceedings/2023/419)] [[CODE](https://github.com/gcfgae/GCFGAE)] - Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/474)] - Dual Personalization on Federated Recommendation. [[PUB](https://www.ijcai.org/proceedings/2023/507)] [[PDF](https://arxiv.org/abs/2301.08143)] [[CODE](https://github.com/zhangcx19/ijcai-23-pfedrec)] - FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity. [[PUB](https://www.ijcai.org/proceedings/2023/492)] [[PDF](https://arxiv.org/abs/2305.05230)] [[CODE](https://github.com/wnn2000/fednoro)] - Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/508)] [[PDF](https://arxiv.org/abs/2304.10783)] [[CODE](https://github.com/zhanghangtao/poisoning-attack-on-fl)] - FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks. [[PUB](https://www.ijcai.org/proceedings/2023/412)] [[PDF](https://arxiv.org/abs/2305.09729)] [[CODE](https://github.com/cynricfu/fedhgn)] - FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer. [[PUB](https://www.ijcai.org/proceedings/2023/443)] [[PDF](https://arxiv.org/abs/2306.15347)] - Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. [[PUB](https://www.ijcai.org/proceedings/2023/393)] [[PDF](https://arxiv.org/abs/2301.09152)] [[CODE](https://github.com/shengchaochen82/metepfl)] - FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training. [[PUB](https://www.ijcai.org/proceedings/2023/483)] [[CODE](https://github.com/Hanzhouu/FedBFPT)] - A Survey of Federated Evaluation in Federated Learning. [[PUB](https://doi.org/10.24963/ijcai.2023/758)] - Learn and Sample Together: Collaborative Generation for Graphic Design Layout. [[PUB](https://doi.org/10.24963/ijcai.2023/649)] - Prompt Learns Prompt: Exploring Knowledge-Aware Generative Prompt Collaboration For Video Captioning. [[PUB](https://doi.org/10.24963/ijcai.2023/180)] - SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [[PUB](https://doi.org/10.24963/ijcai.2023/772)] - Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract). [[PUB](https://doi.org/10.24963/ijcai.2023/790)] #### IJCAI Survey Track - Bayesian Federated Learning: A Survey. [[PDF](https://arxiv.org/abs/2304.13267)] - A Survey of Federated Evaluation in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/758)] [[PDF](https://arxiv.org/abs/2305.08070)] #### IJCAI Journal Track - SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [[PUB](https://www.ijcai.org/proceedings/2023/772)] #### AISTATS - The communication cost of security and privacy in federated frequency estimation. [[PUB](https://proceedings.mlr.press/v206/chen23e.html)] [[CODE](https://colab.research.google.com/drive/1A3sp42a4RKswxjCOBAXlfUxBzL5IF431?usp=share_link)] - Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. [[PUB](https://proceedings.mlr.press/v206/dun23a.html)] [[CODE](https://github.com/dunchen/AsyncDrop__Release)] - Federated Learning under Distributed Concept Drift. [[PUB](https://proceedings.mlr.press/v206/jothimurugesan23a.html)] [[CODE](https://github.com/microsoft/FedDrift)] - Characterizing Internal Evasion Attacks in Federated Learning. [[PUB](https://proceedings.mlr.press/v206/kim23a.html)] [[CODE](https://github.com/tj-kim/pFedDef_v1)] - Federated Asymptotics: a model to compare federated learning algorithms. [[PUB](https://proceedings.mlr.press/v206/cheng23b.html)] [[CODE](https://github.com/garyxcheng/personalized-federated-learning)] - Private Non-Convex Federated Learning Without a Trusted Server. [[PUB](https://proceedings.mlr.press/v206/lowy23a.html)] [[CODE](https://github.com/ghafeleb/Private-NonConvex-Federated-Learning-Without-a-Trusted-Server)] - Federated Learning for Data Streams. [[PUB](https://proceedings.mlr.press/v206/marfoq23a.html)] [[CODE](https://github.com/kholam/FedMuL)] - Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. [[PUB](https://proceedings.mlr.press/v206/mian23a.html)] [[CODE](https://eda.rg.cispa.io/prj/peri/)] - Active Membership Inference Attack under Local Differential Privacy in Federated Learning. [[PUB](https://proceedings.mlr.press/v206/nguyen23e.html)] [[CODE](https://github.com/trucndt/ami)] - Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. [[PUB](https://proceedings.mlr.press/v206/plassier23a.html)] - Byzantine-Robust Federated Learning with Optimal Statistical Rates. [[PUB](https://github.com/wanglun1996/secure-robust-federated-learning)] [[CODE](https://github.com/wanglun1996/secure-robust-federated-learning)] - Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity. [[PUB](https://proceedings.mlr.press/v206/lu23a.html)] - FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. [[PUB](https://proceedings.mlr.press/v206/xu23e.html)] ### 2022 #### ai - Q-Learning-based model predictive variable impedance control for physical human-robot collaboration. [[PUB](https://doi.org/10.1016/j.artint.2022.103771)] #### AISTATS - Towards Understanding Biased Client Selection in Federated Learning. [[PUB](https://proceedings.mlr.press/v151/jee-cho22a.html)] [[CODE](https://proceedings.mlr.press/v151/jee-cho22a/jee-cho22a-supp.zip)] - FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. [[PUB](https://proceedings.mlr.press/v151/gasanov22a.html)] [[PDF](https://arxiv.org/abs/2111.11556)] [[CODE](https://proceedings.mlr.press/v151/gasanov22a/gasanov22a-supp.zip)] - Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. [[PUB](https://proceedings.mlr.press/v151/glasgow22a.html)] [[PDF](https://arxiv.org/abs/2111.03741)] [[CODE](https://github.com/hongliny/sharp-bounds-for-fedavg-and-continuous-perspective)] - Federated Reinforcement Learning with Environment Heterogeneity. [[PUB](https://proceedings.mlr.press/v151/jin22a.html)] [[PDF](https://arxiv.org/abs/2204.02634)] [[CODE](https://github.com/pengyang7881187/fedrl)] - Federated Myopic Community Detection with One-shot Communication. [[PUB](https://proceedings.mlr.press/v151/ke22a.html)] [[PDF](https://arxiv.org/abs/2106.07255)] - Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. [[PUB](https://proceedings.mlr.press/v151/li22e.html)] [[PDF](https://arxiv.org/abs/2110.01463)] [[CODE](https://github.com/cyrilli/Async-LinUCB)] - Towards Federated Bayesian Network Structure Learning with Continuous Optimization. [[PUB](https://proceedings.mlr.press/v151/ng22a.html)] [[PDF](https://arxiv.org/abs/2110.09356)] [[CODE](https://github.com/ignavierng/notears-admm)] - Federated Learning with Buffered Asynchronous Aggregation. [[PUB](https://proceedings.mlr.press/v151/nguyen22b.html)] [[PDF](https://arxiv.org/abs/2106.06639)] [[VIDEO](https://www.youtube.com/watch?v=Ui-OGUAieNY&ab_channel=FederatedLearningOneWorldSeminar)] - Differentially Private Federated Learning on Heterogeneous Data. [[PUB](https://proceedings.mlr.press/v151/noble22a.html)] [[PDF](https://arxiv.org/abs/2111.09278)] [[CODE](https://github.com/maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning)] - SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. [[PUB](https://proceedings.mlr.press/v151/panda22a.html)] [[PDF](https://arxiv.org/abs/2112.06274)] [[CODE](https://github.com/sparsefed/sparsefed)] [[VIDEO](https://www.youtube.com/watch?v=TXG7ZScheas&ab_channel=GoogleTechTalks)] - Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. [[PUB](https://proceedings.mlr.press/v151/qian22a.html)] [[PDF](https://arxiv.org/abs/2111.01847)] - Federated Functional Gradient Boosting. [[PUB](https://proceedings.mlr.press/v151/shen22a.html)] [[PDF](https://arxiv.org/abs/2103.06972)] [[CODE](https://github.com/shenzebang/Federated-Learning-Pytorch)] - QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. [[PUB](https://proceedings.mlr.press/v151/vono22a.html)] [[PDF](https://arxiv.org/abs/2106.00797)] [[CODE](https://proceedings.mlr.press/v151/vono22a/vono22a-supp.zip)] [[VIDEO](https://www.youtube.com/watch?v=fY8V184It1g&ab_channel=FederatedLearningOneWorldSeminar)] - Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time. [[PUB](https://proceedings.mlr.press/v151/deng22a.html)] #### IJCAI - Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting **`kg.`**. [[PUB](https://www.ijcai.org/proceedings/2022/273)] [[PDF](https://doi.org/10.48550/arXiv.2205.04692)] [[CODE](https://github.com/zjukg/maker)] - Personalized Federated Learning With a Graph. [[PUB](https://www.ijcai.org/proceedings/2022/357)] [[PDF](https://arxiv.org/abs/2203.00829)] [[CODE](https://github.com/dawenzi098/SFL-Structural-Federated-Learning)] - Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification. [[PUB](https://www.ijcai.org/proceedings/2022/272)] [[PDF](https://arxiv.org/abs/2005.11903)] - Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/301)] [[PDF](https://arxiv.org/abs/2110.08394)] [[CODE](https://github.com/ljaiverson/pFL-APPLE)] - Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/399)] [[PDF](https://arxiv.org/abs/2204.12703)] - Private Semi-Supervised Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/279)] - Continual Federated Learning Based on Knowledge Distillation. [[PUB](https://doi.org/10.24963/ijcai.2022/306)] - Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features. [[PUB](https://www.ijcai.org/proceedings/2022/308)] [[PDF](https://arxiv.org/abs/2204.13399)] [[CODE](https://github.com/shangxinyi/CReFF-FL)] - Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. [[PUB](https://www.ijcai.org/proceedings/2022/475)] - Personalized Federated Learning with Contextualized Generalization. [[PUB](https://www.ijcai.org/proceedings/2022/311)] [[PDF](https://arxiv.org/abs/2106.13044)] - Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. [[PUB](https://www.ijcai.org/proceedings/2022/106)] [[PDF](https://arxiv.org/abs/2204.13256)] - FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/324)] [[PDF](https://arxiv.org/abs/2111.08211)] [[CODE](https://github.com/FederatedAI/research/tree/main/publications/FedCG)] - FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. [[PUB](https://www.ijcai.org/proceedings/2022/385)] [[PDF](https://arxiv.org/abs/2204.11536)] - Towards Verifiable Federated Learning **`surv.`**. [[PUB](https://www.ijcai.org/proceedings/2022/792)] [[PDF](https://arxiv.org/abs/2202.08310)] - Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting. [[PUB](https://doi.org/10.24963/ijcai.2022/273)] - Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios. [[PUB](https://doi.org/10.24963/ijcai.2022/306)] - Towards Verifiable Federated Learning. [[PUB](https://doi.org/10.24963/ijcai.2022/792)] - A Survey on Gradient Inversion: Attacks, Defenses and Future Directions. [[PUB](https://doi.org/10.24963/ijcai.2022/791)] #### AAAI - HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/19993)] [[PDF](https://arxiv.org/abs/2112.10775)] [[CODE](https://github.com/med-air/HarmoFL)] [[解读](https://zhuanlan.zhihu.com/p/472555067)] - Federated Learning for Face Recognition with Gradient Correction. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20095)] [[PDF](https://arxiv.org/abs/2112.07246)] - SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20643)] [[PDF](https://arxiv.org/abs/2106.02743)] [[CODE](https://github.com/FedML-AI/SpreadGNN)] [[解读](https://zhuanlan.zhihu.com/p/429720860)] - SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20345)] [[CODE](https://github.com/wudonglei99/smartidx)] - Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/19878)] [[PDF](https://arxiv.org/abs/2112.08831)] - Seizing Critical Learning Periods in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20859)] [[PDF](https://arxiv.org/abs/2109.05613)] - Coordinating Momenta for Cross-silo Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20853)] [[PDF](https://arxiv.org/abs/2102.03970)] - FedProto: Federated Prototype Learning over Heterogeneous Devices. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20819)] [[PDF](https://arxiv.org/abs/2105.00243)] [[CODE](https://github.com/yuetan031/fedproto)] - FedSoft: Soft Clustered Federated Learning with Proximal Local Updating. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20785)] [[PDF](https://arxiv.org/abs/2112.06053)] [[CODE](https://github.com/ycruan/FedSoft)] - Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20555)] [[PDF](https://arxiv.org/abs/2112.09824)] [[CODE](https://github.com/bibikar/feddst)] - FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20057)] [[PDF](https://arxiv.org/abs/2112.12496)] [[CODE](https://github.com/jackie840129/fedfr)] - SplitFed: When Federated Learning Meets Split Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20825)] [[PDF](https://arxiv.org/abs/2004.12088)] [[CODE](https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning)] - Efficient Device Scheduling with Multi-Job Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21235)] [[PDF](https://arxiv.org/abs/2112.05928)] - Implicit Gradient Alignment in Distributed and Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20597)] [[PDF](https://arxiv.org/abs/2106.13897)] - Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20775)] [[PDF](https://arxiv.org/abs/2112.07157)] [[CODE](https://github.com/rithram/flynn)] - A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20894)] - Contribution-Aware Federated Learning for Smart Healthcare. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21505)] - Cross-Modal Federated Human Activity Recognition via Modality-Agnostic and Modality-Specific Representation Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20213)] - CrowdFL: A Marketplace for Crowdsourced Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21715)] - FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20903)] - FedProto: Federated Prototype Learning across Heterogeneous Clients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20819)] - Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20755)] - Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21446)] - CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation. [[PUB](https://doi.org/10.1609/aaai.v36i2.20107)] - Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving. [[PUB](https://doi.org/10.1609/aaai.v36i3.20149)] - Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD. [[PUB](https://doi.org/10.1609/aaai.v36i8.20832)] - Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. [[PUB](https://doi.org/10.1609/aaai.v36i9.21177)] - Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration. [[PUB](https://doi.org/10.1609/aaai.v36i4.20372)] - AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21624)] - Class-Wise Adaptive Self Distillation for Federated Learning on Non-IID Data (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21620)] - FedCC: Federated Learning with Consensus Confirmation for Byzantine Attack Resistance (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21627)] #### ALT - Iterated Vector Fields and Conservatism, with Applications to Federated Learning. [[PUB](https://proceedings.mlr.press/v167/charles22a.html)] [[PDF](https://arxiv.org/abs/2109.03973)] ### 2021 #### IJCAI - Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization. [[PUB](https://www.ijcai.org/proceedings/2021/202)] [[PDF](https://arxiv.org/abs/2008.01558)] [[VIDEO](https://papertalk.org/papertalks/35198)] - Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2021/352)] [[PDF](https://arxiv.org/abs/2106.12300)] - FedSpeech: Federated Text-to-Speech with Continual Learning. [[PUB](https://www.ijcai.org/proceedings/2021/527)] [[PDF](https://arxiv.org/abs/2110.07216)] - Practical One-Shot Federated Learning for Cross-Silo Setting. [[PUB](https://www.ijcai.org/proceedings/2021/205)] [[PDF](https://arxiv.org/abs/2010.01017)] [[CODE](https://github.com/QinbinLi/FedKT)] - Federated Model Distillation with Noise-Free Differential Privacy. [[PUB](https://www.ijcai.org/proceedings/2021/216)] [[PDF](https://arxiv.org/abs/2202.08310)] [[VIDEO](https://papertalk.org/papertalks/35184)] - LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. [[PUB](https://www.ijcai.org/proceedings/2021/217)] [[PDF](https://arxiv.org/abs/2007.15789)] - Federated Learning with Fair Averaging. :fire:. [[PUB](https://www.ijcai.org/proceedings/2021/223)] [[PDF](https://arxiv.org/abs/2104.14937)] [[CODE](https://github.com/WwZzz/easyFL)] - H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2021/67)] [[PDF](https://arxiv.org/abs/2106.00275)] - Communication-efficient and Scalable Decentralized Federated Edge Learning. [[PUB](https://www.ijcai.org/proceedings/2021/720)] - Federated Learning with Fair Averaging. [[PUB](https://doi.org/10.24963/ijcai.2021/223)] [[CODE](https://github.com/WwZzz/easyFL)] - Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. [[PUB](https://doi.org/10.24963/ijcai.2021/486)] - Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification. [[PUB](https://doi.org/10.24963/ijcai.2021/135)] [[CODE](https://github.com/Kali-Hac/MG-SCR)] #### AAAI - Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17301)] [[PDF](https://arxiv.org/abs/2103.00958)] [[VIDEO](https://slideslive.com/38947765/secure-bilevel-asynchronous-vertical-federated-learning-with-backward-updating)] - FedRec++: Lossless Federated Recommendation with Explicit Feedback. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16546)] [[VIDEO](https://slideslive.com/38947798/fedrec-lossless-federated-recommendation-with-explicit-feedback)] - Federated Multi-Armed Bandits. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17156)] [[PDF](https://arxiv.org/abs/2101.12204)] [[CODE](https://github.com/ShenGroup/FMAB)] [[VIDEO](https://slideslive.com/38947985/federated-multiarmed-bandits)] - On the Convergence of Communication-Efficient Local SGD for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16920)] [[VIDEO](https://slideslive.com/38948341/on-the-convergence-of-communicationefficient-local-sgd-for-federated-learning)] - FLAME: Differentially Private Federated Learning in the Shuffle Model. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17053)] [[PDF](https://arxiv.org/abs/2009.08063)] [[VIDEO](https://slideslive.com/38948496/flame-differentially-private-federated-learning-in-the-shuffle-model)] [[CODE](https://github.com/Rachelxuan11/FLAME)] - Toward Understanding the Influence of Individual Clients in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17263)] [[PDF](https://arxiv.org/abs/2012.10936)] [[VIDEO](https://slideslive.com/38948549/toward-understanding-the-influence-of-individual-clients-in-federated-learning)] - Provably Secure Federated Learning against Malicious Clients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16849)] [[PDF](https://arxiv.org/abs/2102.01854)] [[VIDEO](https://www.youtube.com/watch?v=LP4uqW18yA0&ab_channel=PurdueCERIAS)] [[SLIDE](https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf)] - Personalized Cross-Silo Federated Learning on Non-IID Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16960)] [[PDF](https://arxiv.org/abs/2007.03797)] [[VIDEO](https://slideslive.com/38948676/personalized-crosssilo-federated-learning-on-noniid-data)] [[UC.](https://github.com/TsingZ0/PFL-Non-IID)] - Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16669)] [[PDF](https://arxiv.org/abs/2010.00753)] [[CODE](https://github.com/kpdonahue/model_sharing_games)] [[VIDEO](https://slideslive.com/38948684/modelsharing-games-analyzing-federated-learning-under-voluntary-participation)] - Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17291)] [[PDF](https://arxiv.org/abs/2102.00655)] [[VIDEO](https://slideslive.com/38949098/curse-or-redemption-how-data-heterogeneity-affects-the-robustness-of-federated-learning)] - Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17093)] [[PDF](https://arxiv.org/abs/2110.12257)] [[CODE](https://github.com/nlokeshiisc/sfedavg-aaai21)] [[VIDEO](https://slideslive.com/38949109/game-of-gradients-mitigating-irrelevant-clients-in-federated-learning)] [[SUPP](https://github.com/nlokeshiisc/SFedAvg-AAAI21)] - Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17240)] [[PDF](https://arxiv.org/abs/2012.13900)] [[VIDEO](https://slideslive.com/38949195/federated-block-coordinate-descent-scheme-for-learning-global-and-personalized-models)] [[CODE](https://github.com/REIYANG/FedBCD)] - Addressing Class Imbalance in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17219)] [[PDF](https://arxiv.org/abs/2008.06217)] [[VIDEO](https://slideslive.com/38949283/adressing-class-imbalance-in-federated-learning)] [[CODE](https://github.com/balanced-fl/Addressing-Class-Imbalance-FL)] [[解读](https://zhuanlan.zhihu.com/p/443009189)] - Defending against Backdoors in Federated Learning with Robust Learning Rate. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17118)] [[PDF](https://arxiv.org/abs/2007.03767)] [[VIDEO](https://slideslive.com/38949344/defending-against-backdoors-in-federated-learning-with-robust-learning-rate)] [[CODE](https://github.com/TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate)] - AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach. [[PUB](https://doi.org/10.1609/aaai.v35i17.17836)] - Collaborative Group Learning. [[PUB](https://doi.org/10.1609/aaai.v35i8.16911)] - Communication-Aware Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v35i8.16838)] - Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning. [[PUB](https://doi.org/10.1609/aaai.v35i12.17246)] - DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning. [[PUB](https://doi.org/10.1609/aaai.v35i2.16204)] - Differentially Private and Communication Efficient Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v35i8.16887)] - Peer Collaborative Learning for Online Knowledge Distillation. [[PUB](https://doi.org/10.1609/aaai.v35i12.17234)] - STL-SGD: Speeding Up Local SGD with Stagewise Communication Period. [[PUB](https://doi.org/10.1609/aaai.v35i11.17153)] - A Serverless Approach to Federated Learning Infrastructure Oriented for IoT/Edge Data Sources (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17870)] #### AISTATS - Free-rider Attacks on Model Aggregation in Federated Learning. [[PUB](http://proceedings.mlr.press/v130/fraboni21a.html)] [[PDF](https://arxiv.org/abs/2006.11901)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning)] [[VIDEO](https://papertalk.org/papertalks/27640)] [[SUPP](http://proceedings.mlr.press/v130/fraboni21a/fraboni21a-supp.pdf)] - Federated f-differential privacy. [[PUB](http://proceedings.mlr.press/v130/zheng21a.html)] [[CODE](https://github.com/enosair/federated-fdp)] [[VIDEO](https://papertalk.org/papertalks/27595)] [[SUPP](http://proceedings.mlr.press/v130/zheng21a/zheng21a-supp.pdf)] - Federated learning with compression: Unified analysis and sharp guarantees :fire:. [[PUB](http://proceedings.mlr.press/v130/haddadpour21a.html)] [[PDF](https://arxiv.org/abs/2007.01154)] [[CODE](https://github.com/MLOPTPSU/FedTorch)] [[VIDEO](https://papertalk.org/papertalks/27584)] [[SUPP](http://proceedings.mlr.press/v130/haddadpour21a/haddadpour21a-supp.pdf)] - Shuffled Model of Differential Privacy in Federated Learning. [[PUB](http://proceedings.mlr.press/v130/girgis21a.html)] [[VIDEO](https://papertalk.org/papertalks/27565)] [[SUPP](http://proceedings.mlr.press/v130/girgis21a/girgis21a-supp.pdf)] - Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. [[PUB](http://proceedings.mlr.press/v130/charles21a.html)] [[PDF](https://arxiv.org/abs/2103.05032)] [[VIDEO](https://papertalk.org/papertalks/27559)] [[SUPP](http://proceedings.mlr.press/v130/charles21a/charles21a-supp.pdf)] - Federated Multi-armed Bandits with Personalization. [[PUB](http://proceedings.mlr.press/v130/shi21c.html)] [[PDF](https://arxiv.org/abs/2102.13101)] [[CODE](https://github.com/ShenGroup/PF_MAB)] [[VIDEO](https://papertalk.org/papertalks/27521)] [[SUPP](http://proceedings.mlr.press/v130/shi21c/shi21c-supp.pdf)] - Towards Flexible Device Participation in Federated Learning. [[PUB](http://proceedings.mlr.press/v130/ruan21a.html)] [[PDF](https://arxiv.org/abs/2006.06954)] [[VIDEO](https://papertalk.org/papertalks/27467)] [[SUPP](http://proceedings.mlr.press/v130/ruan21a/ruan21a-supp.pdf)] - Federated Learning with Compression: Unified Analysis and Sharp Guarantees. [[PUB](http://proceedings.mlr.press/v130/haddadpour21a.html)] - Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. [[PUB](http://proceedings.mlr.press/v130/chen21c.html)] - LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. [[PUB](http://proceedings.mlr.press/v130/shokri-ghadikolaei21a.html)] - Local SGD: Unified Theory and New Efficient Methods. [[PUB](http://proceedings.mlr.press/v130/gorbunov21a.html)] - One-Round Communication Efficient Distributed M-Estimation. [[PUB](http://proceedings.mlr.press/v130/bao21a.html)] ### 2020 #### IJCAI - Federated Meta-Learning for Fraudulent Credit Card Detection. [[PUB](https://www.ijcai.org/proceedings/2020/642)] [[VIDEO](https://www.ijcai.org/proceedings/2020/video/23994)] - A Multi-player Game for Studying Federated Learning Incentive Schemes. [[PUB](https://www.ijcai.org/proceedings/2020/769)] [[CODE](https://github.com/benggggggggg/fedgame)] [[解读](https://zhuanlan.zhihu.com/p/353868739)] - A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition. [[PUB](https://doi.org/10.24963/ijcai.2020/513)] - CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning. [[PUB](https://doi.org/10.24963/ijcai.2020/467)] - Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos. [[PUB](https://doi.org/10.24963/ijcai.2020/68)] #### AAAI - Practical Federated Gradient Boosting Decision Trees. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/5895)] [[PDF](https://arxiv.org/abs/1911.04206)] [[CODE](https://github.com/Xtra-Computing/PrivML)] - Federated Learning for Vision-and-Language Grounding Problems. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/6824)] - Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/6096)] - Federated Patient Hashing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/6121)] - Robust Federated Learning via Collaborative Machine Teaching. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/5826)] [[PDF](https://arxiv.org/abs/1905.02941)] - FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/7021)] [[PDF](https://arxiv.org/abs/2001.06202)] [[CODE](https://github.com/FederatedAI/FedVision)] - Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis. [[PUB](https://doi.org/10.1609/aaai.v34i07.6619)] - Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning. [[PUB](https://doi.org/10.1609/aaai.v34i04.5843)] - Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning. [[PUB](https://doi.org/10.1609/aaai.v34i04.5706)] #### AISTATS - FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. [[PUB](http://proceedings.mlr.press/v108/reisizadeh20a.html)] [[PDF](https://arxiv.org/abs/1909.13014)] [[VIDEO](https://papertalk.org/papertalks/7961)] [[SUPP](http://proceedings.mlr.press/v108/reisizadeh20a/reisizadeh20a-supp.pdf)] - How To Backdoor Federated Learning :fire:. [[PUB](http://proceedings.mlr.press/v108/bagdasaryan20a.html)] [[PDF](https://arxiv.org/abs/1807.00459)] [[VIDEO](https://papertalk.org/papertalks/8046)] [[CODE](https://github.com/ebagdasa/backdoor_federated_learning)] [[SUPP](http://proceedings.mlr.press/v108/bagdasaryan20a/bagdasaryan20a-supp.pdf)] - Federated Heavy Hitters Discovery with Differential Privacy. [[PUB](http://proceedings.mlr.press/v108/zhu20a.html)] [[PDF](https://arxiv.org/abs/1902.08534)] [[VIDEO](https://papertalk.org/papertalks/8129)] [[SUPP](http://proceedings.mlr.press/v108/zhu20a/zhu20a-supp.pdf)] - How To Backdoor Federated Learning. [[PUB](http://proceedings.mlr.press/v108/bagdasaryan20a.html)] - Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction. [[PUB](http://proceedings.mlr.press/v108/li20f.html)] - Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. [[PUB](http://proceedings.mlr.press/v108/zantedeschi20a.html)] - Tighter Theory for Local SGD on Identical and Heterogeneous Data. [[PUB](http://proceedings.mlr.press/v108/bayoumi20a.html)] ### 2019 #### aaai - Collaboration Based Multi-Label Learning. [[PUB](https://doi.org/10.1609/aaai.v33i01.33013550)] - Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks. [[PUB](https://doi.org/10.1609/aaai.v33i01.33015450)] - Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization. [[PUB](https://doi.org/10.1609/aaai.v33i01.33015508)] #### aistats - Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks. [[PUB](http://proceedings.mlr.press/v89/yu19a.html)] - Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. [[PUB](http://proceedings.mlr.press/v89/acharya19a.html)] #### IJCAI - Multi-Agent Visualization for Explaining Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2019/960)] [[VIDEO](https://youtu.be/NPGf_OJrzOg)] - A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification. [[PUB](https://doi.org/10.24963/ijcai.2019/473)] - Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems. [[PUB](https://doi.org/10.24963/ijcai.2019/619)] - Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks. [[PUB](https://doi.org/10.24963/ijcai.2019/359)] - Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. [[PUB](https://doi.org/10.24963/ijcai.2019/538)] - Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees. [[PUB](https://doi.org/10.24963/ijcai.2019/73)] ### 2018 #### aaai - Robust Collaborative Discriminative Learning for RGB-Infrared Tracking. [[PUB](https://doi.org/10.1609/aaai.v32i1.12307)] - Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data. [[PUB](https://doi.org/10.1609/aaai.v32i1.11311)] #### ijcai - Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection. [[PUB](https://doi.org/10.24963/ijcai.2018/285)] - Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment. [[PUB](https://doi.org/10.24963/ijcai.2018/133)] - Collaborative Learning for Weakly Supervised Object Detection. [[PUB](https://doi.org/10.24963/ijcai.2018/135)] - CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering. [[PUB](https://doi.org/10.24963/ijcai.2018/509)] - Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach. [[PUB](https://doi.org/10.24963/ijcai.2018/78)] - Learning and Communicating the Latent States of Human-Machine Collaboration. [[PUB](https://doi.org/10.24963/ijcai.2018/838)] ### 2017 #### aistats - Communication-efficient Distributed Sparse Linear Discriminant Analysis. [[PUB](http://proceedings.mlr.press/v54/tian17a.html)] - Decentralized Collaborative Learning of Personalized Models over Networks. [[PUB](http://proceedings.mlr.press/v54/vanhaesebrouck17a.html)] ### 2016 #### ai - H-index manipulation by merging articles: Models, theory, and experiments. [[PUB](https://doi.org/10.1016/j.artint.2016.08.001)] #### aistats - Communication Efficient Distributed Agnostic Boosting. [[PUB](http://proceedings.mlr.press/v51/chen16e.html)] #### ijcai - Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction. [[PUB](http://www.ijcai.org/Abstract/16/424)] - Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior. [[PUB](http://www.ijcai.org/Abstract/16/382)] ### 2015 #### ijcai - H-Index Manipulation by Merging Articles: Models, Theory, and Experiments. [[PUB](http://ijcai.org/Abstract/15/119)] ### 2014 #### aistats - Scalable Collaborative Bayesian Preference Learning. [[PUB](http://proceedings.mlr.press/v33/khan14.html)] [[CODE](https://github.com/UKPLab/tacl2018-preference-convincing/tree/crowdGPPL)] ### 2013 #### aaai - Learning Collaborative Impedance-Based Robot Behaviors. [[PUB](https://doi.org/10.1609/aaai.v27i1.8543)] #### ai - Transfer learning in heterogeneous collaborative filtering domains. [[PUB](https://doi.org/10.1016/j.artint.2013.01.003)] ### 2012 #### aaai - Transfer Learning in Collaborative Filtering with Uncertain Ratings. [[PUB](https://doi.org/10.1609/aaai.v26i1.8197)] #### ai - Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. [[PUB](https://doi.org/10.1016/j.artint.2012.02.002)] ### 2011 #### aaai - Mechanism Design for Federated Sponsored Search Auctions. [[PUB](http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3576)] ### 2010 #### aaai - Transfer Learning in Collaborative Filtering for Sparsity Reduction. [[PUB](https://doi.org/10.1609/aaai.v24i1.7578)] ### 2007 #### aaai - A Model-based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic. [[PUB](http://www.aaai.org/Library/AAAI/2007/aaai07-074.php)] - PLOW: A Collaborative Task Learning Agent. [[PUB](http://www.aaai.org/Library/AAAI/2007/aaai07-240.php)] ### 2000 #### alt - Extracting Information from the Web for Concept Learning and Collaborative Filtering. [[PUB](https://doi.org/10.1007/3-540-40992-0_1)]</details> ## fl in top ml conference and journal Federated Learning papers accepted by top ML(machine learning) conference and journal, Including [NeurIPS](https://dblp.uni-trier.de/db/conf/nips/index.html)(Annual Conference on Neural Information Processing Systems), [ICML](https://dblp.uni-trier.de/db/conf/icml/index.html)(International Conference on Machine Learning), [ICLR](https://dblp.uni-trier.de/db/conf/iclr/index.html)(International Conference on Learning Representations), [COLT](https://dblp.org/db/conf/colt/index.html)(Annual Conference Computational Learning Theory) , [UAI](https://dblp.org/db/conf/uai/index.html)(Conference on Uncertainty in Artificial Intelligence),[Machine Learning](https://dblp.org/db/journals/ml/index.html), [JMLR](https://dblp.uni-trier.de/db/journals/jmlr/index.html)(Journal of Machine Learning Research), [TPAMI](https://dblp.uni-trier.de/db/journals/pami/index.html)(IEEE Transactions on Pattern Analysis and Machine Intelligence). - [NeurIPS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANeurIPS%3A) [2024](https://papers.nips.cc/paper_files/paper/2024)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2024/Conference#tab-accept-oral)), [2023](https://papers.nips.cc/paper_files/paper/2023)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2023/Conference#tab-accept-oral)), [2022](https://papers.nips.cc/paper_files/paper/2022)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2022/Conference)), [2021](https://papers.nips.cc/paper/2021)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2021/Conference)), [2020](https://papers.nips.cc/paper/2020), [2018](https://papers.nips.cc/paper/2018), [2017](https://papers.nips.cc/paper/2017) - [ICML](https://dblp.uni-trier.de/search?q=federate%20venue%3AICML%3A) [2025](https://icml.cc/Conferences/2025/Schedule?type=Poster), [2024](https://icml.cc/Conferences/2024/Schedule?type=Poster), [2023](https://icml.cc/Conferences/2023/Schedule?type=Poster), [2022](https://icml.cc/Conferences/2022/Schedule?type=Poster), [2021](https://icml.cc/Conferences/2021/Schedule?type=Poster), [2020](https://icml.cc/Conferences/2020/Schedule?type=Poster), [2019](https://icml.cc/Conferences/2019/Schedule?type=Poster) - [ICLR](https://dblp.uni-trier.de/search?q=federate%20venue%3AICLR%3A) [2025](https://openreview.net/group?id=ICLR.cc/2025/Conference), [2024](https://openreview.net/group?id=ICLR.cc/2024/Conference), [2023](https://openreview.net/group?id=ICLR.cc/2023/Conference), [2022](https://openreview.net/group?id=ICLR.cc/2022/Conference), [2021](https://openreview.net/group?id=ICLR.cc/2021/Conference), [2020](https://openreview.net/group?id=ICLR.cc/2020/Conference) - [COLT](https://dblp.org/search?q=federated%20venue%3ACOLT%3A) [2023](https://proceedings.mlr.press/v195/) - [UAI](https://dblp.org/search?q=federated%20venue%3AUAI%3A) [2025](https://www.auai.org/uai2025/accepted_papers), [2024](https://www.auai.org/uai2024/accepted_papers), [2023](https://www.auai.org/uai2023/accepted_papers), [2022](https://www.auai.org/uai2022/accepted_papers), [2021](https://www.auai.org/uai2021/accepted_papers) - [Machine Learning](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fml%3A) 2026, 2025, 2024, 2023, 2022 - [JMLR](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fjmlr%3A) 2025([v26](https://jmlr.org/papers/v26/)), 2024([v25](https://jmlr.org/papers/v25/)), 2023([v24](https://jmlr.org/papers/v24/)), 2021([v22](https://jmlr.org/papers/v22/)) - [TPAMI](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fpami%3A) 2026, 2025, 2024, 2023, 2022 <details open> <summary>fl in top ml conference and journal</summary> <!-- START:fl-in-top-ml-conference-and-journal --> <!-- END:fl-in-top-ml-conference-and-journal --> ### 2026 #### jmlr - Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information. [[PUB](https://jmlr.org/papers/v27/23-0440.html)] #### Machine Learning - FDGReID: Federated Domain Generalization for Person Re-identification. [[PUB](https://doi.org/10.1007/s10994-025-06974-z)] - FedBNR: A Fully Global Federated Gaussian Process. [[PUB](https://doi.org/10.1007/s10994-025-06936-5)] - Federated Learning on Riemannian Manifolds with Differential Privacy. [[PUB](https://doi.org/10.1007/s10994-026-07018-w)] - Federated SHAP: Privacy-Preserving and Consistent Post-hoc Explainability in Federated Learning. [[PUB](https://doi.org/10.1007/s10994-025-06956-1)] - FedGES: A Federated Learning Approach for Bayesian Network Structure Learning. [[PUB](https://doi.org/10.1007/s10994-025-06939-2)] - Collaborative Multivariate Time Series Forecasting via Variable-Tailored Inter-temporal Graph and Adaptive-Smooth Frequency Fusion. [[PUB](https://doi.org/10.1007/s10994-025-06963-2)] - PC-MoE: memory-efficient and privacy-preserving collaborative training for Mixture-of-Experts LLMs. [[PUB](https://doi.org/10.1007/s10994-025-06901-2)] #### TPAMI - A Bayesian Framework for Clustered Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3637562)] - Adaptive Batch Size Time Evolving Stochastic Gradient Descent for Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3610169)] - Communication-Efficient Federated Multi-View Clustering. [[PUB](https://doi.org/10.1109/TPAMI.2025.3601533)] - Decentralized Federated Learning With Distributed Aggregation Weight Optimization. [[PUB](https://doi.org/10.1109/TPAMI.2025.3640709)] - Exploring the Vulnerabilities of Federated Learning: A Deep Dive Into Gradient Inversion Attacks. [[PUB](https://doi.org/10.1109/TPAMI.2025.3646639)] - FedFask: Fast Sketching Distributed PCA for Large-Scale Federated Data. [[PUB](https://doi.org/10.1109/TPAMI.2025.3639635)] - Sample-Level Prototypical Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3612302)] - Slack Federated Adversarial Training. [[PUB](https://doi.org/10.1109/TPAMI.2025.3646649)] - Toward Understanding Generalization and Stability Gaps Between Centralized and Decentralized Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3647762)] - Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging. [[PUB](https://doi.org/10.1109/TPAMI.2025.3629605)] ### 2025 #### JMLR - Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback. [[PUB](https://jmlr.org/papers/v26/24-0385.html)] - Client Selection for Federated Policy Optimization with Environment Heterogeneity. [[PUB](https://jmlr.org/papers/v26/24-0233.html)] - FedHB: Hierarchical Bayesian Federated Learning. [[PUB](https://jmlr.org/papers/v26/23-1350.html)] - PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark. [[PUB](https://jmlr.org/papers/v26/23-1634.html)] - Sharp Bounds for Sequential Federated Learning on Heterogeneous Data. [[PUB](https://jmlr.org/papers/v26/24-0668.html)] - Collaborative likelihood-ratio estimation over graphs. [[PUB](https://jmlr.org/papers/v26/24-0565.html)] #### machine learning - Auction-based incentive mechanism with personalized privacy protection in federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06836-8)] - DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy. [[PUB](https://doi.org/10.1007/s10994-025-06888-w)] - Efficient federated unlearning under plausible deniability. [[PUB](https://doi.org/10.1007/s10994-024-06685-x)] [[CODE](https://github.com/Ayush-Umu/Federated-Unlearning-under-Plausible-Deniability)] - Federated causal inference from observational data. [[PUB](https://doi.org/10.1007/s10994-025-06819-9)] - Fedflow: a personalized federated learning framework for passenger flow prediction. [[PUB](https://doi.org/10.1007/s10994-025-06795-0)] - FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06861-7)] - HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06804-2)] - Improve global generalization for personalized federated learning within a Stackelberg game. [[PUB](https://doi.org/10.1007/s10994-025-06770-9)] - TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [[PUB](https://doi.org/10.1007/s10994-025-06805-1)] - Adaptive collaborative minority oversampling for multi-class imbalanced classification. [[PUB](https://doi.org/10.1007/s10994-025-06899-7)] - Multi-modal co-learning for Earth observation: enhancing single-modality models via modality collaboration. [[PUB](https://doi.org/10.1007/s10994-025-06903-0)] #### UAI - Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication. [[PUB](https://proceedings.mlr.press/v286/zhang25f.html)] - FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning. [[PUB](https://proceedings.mlr.press/v286/tang25a.html)] - FeDCM: Federated Learning of Deep Causal Generative Models. [[PUB](https://proceedings.mlr.press/v286/rahman25a.html)] - Federated Rényi Fair Inference in Federated Heterogeneous System. [[PUB](https://proceedings.mlr.press/v286/ma25a.html)] - FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning. [[PUB](https://proceedings.mlr.press/v286/lin25a.html)] - ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. [[PUB](https://proceedings.mlr.press/v286/karagulyan25a.html)] - FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set. [[PUB](https://proceedings.mlr.press/v286/ibrahim25a.html)] - Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning. [[PUB](https://proceedings.mlr.press/v286/diana25a.html)] - Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information. [[PUB](https://proceedings.mlr.press/v286/akgul25a.html)] - Hindsight Merging: Diverse Data Generation with Language Models. [[PUB](https://proceedings.mlr.press/v286/veselovsky25a.html)] #### ICML - Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off. [[PUB](https://openreview.net/forum?id=C7dmhyTDrx)] [[CODE](https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other)] - Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective. [[PUB](https://openreview.net/forum?id=wleRTUQj07)] - SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding. [[PUB](https://openreview.net/forum?id=j7H4mbeOI1)] [[CODE](https://github.com/NusIoraPrivacy/SecEmb)] - Causality Inspired Federated Learning for OOD Generalization. [[PUB](https://openreview.net/forum?id=pWWUJw2qew)] [[CODE](https://github.com/BIT-DA/CIRL)] - Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging. [[PUB](https://openreview.net/forum?id=HqmXiuFaOr)] [[CODE](https://github.com/junkangLiu0/FedSWA)] - One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models. [[PUB](https://openreview.net/forum?id=PqJFVbJAMR)] [[CODE](https://github.com/HaokunChen245/FedBiP)] - FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. [[PUB](https://openreview.net/forum?id=XCLZgbm99O)] - An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective. [[PUB](https://openreview.net/forum?id=eLkkXaPFEP)] [[CODE](https://github.com/5Martina5/ESFMC)] - Gap-Dependent Bounds for Federated $Q$-Learning. [[PUB](https://openreview.net/forum?id=0n2nXmOxZS)] - FedBEns: One-Shot Federated Learning based on Bayesian Ensemble. [[PUB](https://openreview.net/forum?id=oTCiv1bkjG)] [[CODE](https://github.com/jacopot96/FedBEns)] - NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel. [[PUB](https://openreview.net/forum?id=hC7zCFk5Dp)] [[CODE](https://github.com/Gabe-Thomp/ntk-dfl)] - Federated Learning for Feature Generalization with Convex Constraints. [[PUB](https://openreview.net/forum?id=pI4AbQ7pg1)] [[CODE](https://github.com/skku-dhkim/FedTorch.git)] - Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos. [[PUB](https://openreview.net/forum?id=EU5lci90fF)] [[CODE](https://github.com/destiny301/uefl)] - Towards Trustworthy Federated Learning with Untrusted Participants. [[PUB](https://openreview.net/forum?id=PjadKnUson)] - Multi-Session Budget Optimization for Forward Auction-based Federated Learning. [[PUB](https://openreview.net/forum?id=bFB0N8ABIr)] - Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation. [[PUB](https://openreview.net/forum?id=0p86Mhg014)] [[CODE](https://github.com/MoratalYang/FedDDA)] - LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation. [[PUB](https://openreview.net/forum?id=li59703WbA)] - Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning. [[PUB](https://openreview.net/forum?id=cgHfR7bt0V)] - Differentially Private Federated $k$-Means Clustering with Server-Side Data. [[PUB](https://openreview.net/forum?id=EFLPHl5RGJ)] [[CODE](https://github.com/jonnyascott/fed-dp-kmeans)] - CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning. [[PUB](https://openreview.net/forum?id=lvkVhZ776k)] - Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees. [[PUB](https://openreview.net/forum?id=MM6ZWF7gl9)] [[CODE](https://github.com/ZLHe0/fedclup)] - $S^2$FGL: Spatial Spectral Federated Graph Learning. [[PUB](https://openreview.net/forum?id=pFQ3MnyIT6)] [[CODE](https://github.com/Wonder7racer/S2FGL.git)] - FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation. [[PUB](https://openreview.net/forum?id=HnwcrtoDd4)] [[CODE](https://github.com/srijith1996/FSL-SAGE)] - Interaction-Aware Gaussian Weighting for Clustered Federated Learning. [[PUB](https://openreview.net/forum?id=dZAQxNFKGg)] [[CODE](https://openreview.net/forum?id=XCLZgbm99O)] - Efficient Heterogeneity-Aware Federated Active Data Selection. [[PUB](https://openreview.net/forum?id=pSdWTED0ZZ)] - Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models. [[PUB](https://openreview.net/forum?id=ny0m8YEUzH)] [[CODE](https://github.com/liaosunny123/FedICU)] - Rethinking the Temperature for Federated Heterogeneous Distillation. [[PUB](https://openreview.net/forum?id=f9xsNQ8oSd)] - FedClean: A General Robust Label Noise Correction for Federated Learning. [[PUB](https://openreview.net/forum?id=4kF2ZZcePc)] - Federated Causal Structure Learning with Non-identical Variable Sets. [[PUB](https://openreview.net/forum?id=QlEx8f3S61)] - FedECADO: A Dynamical System Model of Federated Learning. [[PUB](https://openreview.net/forum?id=gujuGnbhZr)] - Efficient Federated Incomplete Multi-View Clustering. [[PUB](https://openreview.net/forum?id=sylDbssCU9)] [[CODE](https://github.com/Tracesource/EFIMVC)] - Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance. [[PUB](https://openreview.net/forum?id=7qvYLnJDRd)] [[CODE](https://github.com/PaddiHunter/FIMCFG)] - Local Pan-privacy for Federated Analytics. [[PUB](https://openreview.net/forum?id=M18dhHTFf8)] - FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning. [[PUB](https://openreview.net/forum?id=QwTDQXllam)] [[CODE](https://github.com/GanyuWang/FedOne-BDPL)] - Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning. [[PUB](https://openreview.net/forum?id=zV5pkTMHPP)] [[CODE](https://github.com/Hongyao-Chen/HybridBN)] - Private Federated Learning using Preference-Optimized Synthetic Data. [[PUB](https://openreview.net/forum?id=ZuaU2bYzlc)] [[CODE](https://github.com/meiyuw/POPri)] - Enhancing Foundation Models with Federated Domain Knowledge Infusion. [[PUB](https://openreview.net/forum?id=6SIVFmjIm4)] - FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation. [[PUB](https://openreview.net/forum?id=y7pDvbi9xz)] [[CODE](https://github.com/CYFang6/FedPHA)] - Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework. [[PUB](https://openreview.net/forum?id=jwjvkWsePB)] [[CODE](https://app.box.com/s/phf6bhjy6owcr6b1rvfe412fiw059pxk)] - Federated Node-Level Clustering Network with Cross-Subgraph Link Mending. [[PUB](https://openreview.net/forum?id=38Nh0TebXZ)] - Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [[PUB](https://openreview.net/forum?id=mzPArjGqrs)] [[CODE](https://github.com/allen4747/Ferret)] - FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence. [[PUB](https://openreview.net/forum?id=9hFQvmCl7P)] - Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework. [[PUB](https://openreview.net/forum?id=M7mVzCV6uU)] [[CODE](https://github.com/Terje-M/FedGVI)] - DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence. [[PUB](https://openreview.net/forum?id=EvzArsKUww)] - On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists. [[PUB](https://openreview.net/forum?id=Eog0kXX7hW)] - Safe-EF: Error Feedback for Non-smooth Constrained Optimization. [[PUB](https://openreview.net/forum?id=9D5aM5LQ3Y)] [[CODE](https://github.com/yardenas/safe-ef)] - Gradient Inversion of Multimodal Models. [[PUB](https://openreview.net/forum?id=j4IELrBhoG)] [[CODE](https://github.com/AlonZolfi/gi-dqa)] - Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg. [[PUB](https://openreview.net/forum?id=0p04srg7uf)] [[CODE](https://github.com/kkhuge/ICML2025)] - Decoupled SGDA for Games with Intermittent Strategy Communication. [[PUB](https://openreview.net/forum?id=ZYkFTSEZ6k)] - Private Model Personalization Revisited. [[PUB](https://openreview.net/forum?id=hw1kGPcSZ5)] - Leveraging Randomness in Model and Data Partitioning for Privacy Amplification. [[PUB](https://openreview.net/forum?id=3K6BkFZ7ka)] - Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. [[PUB](https://openreview.net/forum?id=2XvOJvUlKc)] [[CODE](https://github.com/pmangold/scaffold-speed-up)] - Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning. [[PUB](https://openreview.net/forum?id=hrBfufwMzg)] [[CODE](https://github.com/buptcmm/phnhvvs)] - FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates. [[PUB](https://openreview.net/forum?id=V18WOxHRMq)] [[CODE](https://github.com/lxy66888/fedsmu.git)] - One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory. [[PUB](https://openreview.net/forum?id=80mK2Mqaph)] [[CODE](https://github.com/harrylee999/FL-SAM)] - Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts. [[PUB](https://openreview.net/forum?id=dKfq3JbjnE)] - Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning. [[PUB](https://openreview.net/forum?id=2XvF67vbCK)] [[CODE](https://github.com/zenghui9977/FAFI_ICML25)] - GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention. [[PUB](https://openreview.net/forum?id=nAk0ENu8LS)] [[CODE](https://github.com/JiaruQian/GHOST)] - DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing. [[PUB](https://openreview.net/forum?id=Nv6mOSqUVA)] - BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach. [[PUB](https://openreview.net/forum?id=fmlol78Qqf)] - Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning. [[PUB](https://openreview.net/forum?id=HdS6tZwwa7)] - EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification. [[PUB](https://openreview.net/forum?id=Bd9JlrqZhN)] [[CODE](https://github.com/ZitongShi/EAGLES)] - Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference. [[PUB](https://openreview.net/forum?id=Zn6hmmBnAa)] [[CODE](https://github.com/mahendrathapa/pFedHB)] - Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models. [[PUB](https://openreview.net/forum?id=R7gCixl2xR)] [[CODE](https://github.com/GivralNguyen/FL-LDP-AMI)] - Generalization in Federated Learning: A Conditional Mutual Information Framework. [[PUB](https://openreview.net/forum?id=kOttDCDYJp)] - The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=aooq3tQIX9)] [[CODE](https://github.com/Leopold1423/fedmud-icml25)] - Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions. [[PUB](https://openreview.net/forum?id=rCJNbDXkvC)] [[CODE](https://github.com/dcll-iiitd/CoresetForVFL)] - Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. [[PUB](https://openreview.net/forum?id=ULZHqJU4ZC)] - Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. [[PUB](https://openreview.net/forum?id=TUk7gCqtmf)] - SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution. [[PUB](https://openreview.net/forum?id=Kjz03pmyW0)] [[CODE](https://github.com/WenddHe0119/SPMC)] - You Get What You Give: Reciprocally Fair Federated Learning. [[PUB](https://openreview.net/forum?id=ZdmMDz33Io)] - Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead. [[PUB](https://openreview.net/forum?id=6znPjYn11w)] [[CODE](https://github.com/pupiu45/FedGO)] - Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning. [[PUB](https://openreview.net/forum?id=iBOMvaa2aN)] - Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts. [[PUB](https://proceedings.mlr.press/v267/li25eb.html)] - Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks. [[PUB](https://proceedings.mlr.press/v267/tastan25a.html)] - BECAME: Bayesian Continual Learning with Adaptive Model Merging. [[PUB](https://proceedings.mlr.press/v267/li25bk.html)] - BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly. [[PUB](https://proceedings.mlr.press/v267/shen25i.html)] - Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging. [[PUB](https://proceedings.mlr.press/v267/chen25cm.html)] - CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging. [[PUB](https://proceedings.mlr.press/v267/yang25x.html)] - CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging. [[PUB](https://proceedings.mlr.press/v267/sun25i.html)] - Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold. [[PUB](https://proceedings.mlr.press/v267/song25c.html)] - Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. [[PUB](https://proceedings.mlr.press/v267/gotz25a.html)] - HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training. [[PUB](https://proceedings.mlr.press/v267/kim25y.html)] - Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent. [[PUB](https://proceedings.mlr.press/v267/wei25k.html)] - Mutual Learning for SAM Adaptation: A Dual Collaborative Network Framework for Source-Free Domain Transfer. [[PUB](https://proceedings.mlr.press/v267/liu25ca.html)] - No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces. [[PUB](https://proceedings.mlr.press/v267/marczak25a.html)] [[CODE](https://github.com/danielm1405/iso-merging)] - Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging. [[PUB](https://proceedings.mlr.press/v267/chen25af.html)] - Representation Surgery in Model Merging with Probabilistic Modeling. [[PUB](https://proceedings.mlr.press/v267/wei25c.html)] - Scalable Model Merging with Progressive Layer-wise Distillation. [[PUB](https://proceedings.mlr.press/v267/xu25r.html)] - ToMA: Token Merge with Attention for Diffusion Models. [[PUB](https://proceedings.mlr.press/v267/lu25v.html)] [[CODE](https://github.com/WenboLuu/ToMA)] - Whoever Started the interference Should End It: Guiding Data-Free Model Merging via Task Vectors. [[PUB](https://proceedings.mlr.press/v267/cheng25h.html)] #### Mach Learn - HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06804-2)] - Fedflow: a personalized federated learning framework for passenger flow prediction. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06795-0)] - Federated causal inference from observational data. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06819-9)] - TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06805-1)] - Improve global generalization for personalized federated learning within a Stackelberg game. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06770-9)] - Efficient federated unlearning under plausible deniability. [[PUB](https://link.springer.com/article/10.1007/s10994-024-06685-x)] [[CODE](https://github.com/Ayush-Umu/Federated-Unlearning-under-Plausible-Deniability)] - Auction-based incentive mechanism with personalized privacy protection in federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06836-8)] - DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy. [[PUB](https://doi.org/10.1007/s10994-025-06888-w)] - FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06861-7)] #### ICLR - Energy-based Backdoor Defense Against Federated Graph Learning. [[PUB](https://openreview.net/forum?id=5Jc7r5aqHJ)] - DEPT: Decoupled Embeddings for Pre-training Language Models. [[PUB](https://openreview.net/forum?id=vf5aUZT0Fz)] - Subgraph Federated Learning for Local Generalization. [[PUB](https://openreview.net/forum?id=cH65nS5sOz)] [[CODE](https://github.com/sung-won-kim/FedLoG)] - Problem-Parameter-Free Federated Learning. [[PUB](https://openreview.net/forum?id=ZuazHmXTns)] - Adaptive Gradient Clipping for Robust Federated Learning. [[PUB](https://openreview.net/forum?id=03OkC0LKDD)] - Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees. [[PUB](https://openreview.net/forum?id=cznqgb4DNv)] - LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. [[PUB](https://openreview.net/forum?id=PpYy0dR3Qw)] - Group Distributionally Robust Dataset Distillation with Risk Minimization. [[PUB](https://openreview.net/forum?id=3JsU5QXNru)] - GRAIN: Exact Graph Reconstruction from Gradients. [[PUB](https://openreview.net/forum?id=7bAjVh3CG3)] - Towards Faster Decentralized Stochastic Optimization with Communication Compression. [[PUB](https://openreview.net/forum?id=CMMpcs9prj)] - Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. [[PUB](https://openreview.net/forum?id=Bl3e8HV9xW)] - Many-Objective Multi-Solution Transport. [[PUB](https://openreview.net/forum?id=Neb17mimVH)] - Query-based Knowledge Transfer for Heterogeneous Learning Environments. [[PUB](https://openreview.net/forum?id=XKv29sMyjF)] - Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting. [[PUB](https://openreview.net/forum?id=ydREOIttdC)] - Federated Granger Causality Learning For Interdependent Clients With State Space Representation. [[PUB](https://openreview.net/forum?id=KTgQGXz5xj)] - Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization. [[PUB](https://openreview.net/forum?id=omrLHFzC37)] - Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. [[PUB](https://openreview.net/forum?id=TrJ36UfD9P)] - On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=7pDI74iOyu)] - PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models. [[PUB](https://openreview.net/forum?id=B9kUJuWrYC)] - Differentially Private Federated Learning with Time-Adaptive Privacy Spending. [[PUB](https://openreview.net/forum?id=W0nydevOlG)] - Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies. [[PUB](https://openreview.net/forum?id=zPDpdk3V8L)] - Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis. [[PUB](https://openreview.net/forum?id=5DUekOKWcS)] - On the Byzantine-Resilience of Distillation-Based Federated Learning. [[PUB](https://openreview.net/forum?id=of6EuHT7de)] - Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [[PUB](https://openreview.net/forum?id=sYNWqQYJhz)] - Event-Driven Online Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=FCBbh0HCrF)] - On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations. [[PUB](https://openreview.net/forum?id=BfUDZGqCAu)] - Federated Domain Generalization with Data-free On-server Matching Gradient. [[PUB](https://openreview.net/forum?id=8TERgu1Lb2)] - Unlocking the Potential of Model Calibration in Federated Learning. [[PUB](https://openreview.net/forum?id=Osr0KZJeTX)] - FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking. [[PUB](https://openreview.net/forum?id=6RjQ54M1rM)] - Understanding the Stability-based Generalization of Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=znhZbonEoe)] - Federated Residual Low-Rank Adaption of Large Language Models. [[PUB](https://openreview.net/forum?id=e0rQRMUhs7)] - FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine. [[PUB](https://openreview.net/forum?id=44hcrfzydU)] - Vertical Federated Learning with Missing Features During Training and Inference. [[PUB](https://openreview.net/forum?id=OXi1FmHGzz)] [[CODE](https://github.com/Valdeira/LASER-VFL)] - Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost. [[PUB](https://openreview.net/forum?id=FoUpv84hMw)] - Selective Aggregation for Low-Rank Adaptation in Federated Learning. [[PUB](https://openreview.net/forum?id=iX3uESGdsO)] [[CODE](https://github.com/Pengxin-Guo/FedSA-LoRA)] - Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models. [[PUB](https://openreview.net/forum?id=Equ277PBN0)] - Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. [[PUB](https://openreview.net/forum?id=B8akWa62Da)] - Debiasing Federated Learning with Correlated Client Participation. [[PUB](https://openreview.net/forum?id=9h45qxXEx0)] - Decoupled Subgraph Federated Learning. [[PUB](https://openreview.net/forum?id=v1rFkElnIn)] - Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=79nO2DPjVX)] - Towards Federated RLHF with Aggregated Client Preference for LLMs. [[PUB](https://openreview.net/forum?id=mqNKiEB6pd)] - SparsyFed: Sparse Adaptive Federated Learning. [[PUB](https://openreview.net/forum?id=OBUQNASaWw)] - Can Textual Gradient Work in Federated Learning?. [[PUB](https://openreview.net/forum?id=Cy5IKvYbR3)] - Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models. [[PUB](https://openreview.net/forum?id=xiDJaTim3P)] [[CODE](https://github.com/ljaiverson/pFedMoAP)] - Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning. [[PUB](https://openreview.net/forum?id=3wEGdrV5Cb)] - Connecting Federated ADMM to Bayes. [[PUB](https://openreview.net/forum?id=ipQrjRsl11)] - Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning. [[PUB](https://openreview.net/forum?id=ROpY0qRUXL)] - Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond. [[PUB](https://openreview.net/forum?id=f65RuQgVlp)] - Federated Few-Shot Class-Incremental Learning. [[PUB](https://openreview.net/forum?id=ZiPoAlKf9Y)] - Federated Residual Low-Rank Adaptation of Large Language Models. [[PUB](https://openreview.net/forum?id=e0rQRMUhs7)] - Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models. [[PUB](https://openreview.net/forum?id=sdLGY9Dj5r)] - EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models. [[PUB](https://openreview.net/forum?id=xtlMtbVfWu)] - LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging. [[PUB](https://openreview.net/forum?id=J5sUOvlLbQ)] [[CODE](https://github.com/wang-kee/LiNeS)] - MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. [[PUB](https://openreview.net/forum?id=1v7SRWsYve)] - Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning. [[PUB](https://openreview.net/forum?id=eaTqsptDPL)] [[CODE](https://github.com/baiklab/SAFT-Merge)] - Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace. [[PUB](https://openreview.net/forum?id=dqMqAaw7Sq)] [[CODE](https://github.com/Yangjinluan/DAM)] - Model merging with SVD to tie the Knots. [[PUB](https://openreview.net/forum?id=67X93aZHII)] [[CODE](https://github.com/gstoica27/KnOTS)] - MrT5: Dynamic Token Merging for Efficient Byte-level Language Models. [[PUB](https://openreview.net/forum?id=VYWBMq1L7H)] - Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine. [[PUB](https://openreview.net/forum?id=pH543jrbe8)] - REMEDY: Recipe Merging Dynamics in Large Vision-Language Models. [[PUB](https://openreview.net/forum?id=iX7eHHE5Tx)] - Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models. [[PUB](https://openreview.net/forum?id=mQ55y4s5hj)] #### TPAMI - DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3546659)] - Federated Multi-View K-Means Clustering. [[PUB](https://doi.org/10.1109/TPAMI.2024.3520708)] - FedID: Enhancing Federated Learning Security Through Dynamic Identification. [[PUB](https://doi.org/10.1109/TPAMI.2025.3581555)] - Medical Federated Model With Mixture of Personalized and Shared Components. [[PUB](https://doi.org/10.1109/TPAMI.2024.3470072)] - Re-Fed+: A Better Replay Strategy for Federated Incremental Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3551732)] - Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients. [[PUB](https://doi.org/10.1109/TPAMI.2025.3527137)] - Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation. [[PUB](https://doi.org/10.1109/TPAMI.2024.3469188)] - Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy. [[PUB](https://doi.org/10.1109/TPAMI.2025.3597922)] - VQ-FedDiff: Federated Learning Algorithm of Diffusion Models With Client-Specific Vector-Quantized Conditioning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3602282)] - NICE: Improving Panoptic Narrative Detection and Segmentation With Cascading Collaborative Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3583795)] ### 2024 #### colt - The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. [[PUB](https://proceedings.mlr.press/v247/patel24a.html)] #### machine learning - Aligning model outputs for class imbalanced non-IID federated learning. [[PUB](https://doi.org/10.1007/s10994-022-06241-5)] - Communication-efficient clustered federated learning via model distance. [[PUB](https://doi.org/10.1007/s10994-023-06443-5)] - Federated learning with superquantile aggregation for heterogeneous data. [[PUB](https://doi.org/10.1007/s10994-023-06332-x)] - Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [[PUB](https://doi.org/10.1007/s10994-024-06541-y)] #### UAI - FedAST: Federated Asynchronous Simultaneous Training. [[PUB](https://proceedings.mlr.press/v244/askin24a.html)] - On Convergence of Federated Averaging Langevin Dynamics. [[PUB](https://proceedings.mlr.press/v244/deng24a.html)] - On the Convergence of Hierarchical Federated Learning with Partial Worker Participation. [[PUB](https://proceedings.mlr.press/v244/jiang24a.html)] - Pure Exploration in Asynchronous Federated Bandits. [[PUB](https://proceedings.mlr.press/v244/wang24c.html)] #### NeurIPS - One-shot Federated Learning via Synthetic Distiller-Distillate Communication. [[PUB](https://openreview.net/forum?id=6292sp7HiE)] - Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. [[PUB](https://openreview.net/forum?id=uO53206oLJ)] - FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation. [[PUB](https://openreview.net/forum?id=c3OZBJpN7M)] - Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach. [[PUB](https://openreview.net/forum?id=6lx34fpanw)] - Federated Model Heterogeneous Matryoshka Representation Learning. [[PUB](https://openreview.net/forum?id=5yboFMpvHf)] - Federated Graph Learning for Cross-Domain Recommendation. [[PUB](https://openreview.net/forum?id=UBpPOqrBKE)] - FedGMark: Certifiably Robust Watermarking for Federated Graph Learning. [[PUB](https://openreview.net/forum?id=xeviQPXTMU)] - Dual-Personalizing Adapter for Federated Foundation Models. [[PUB](https://openreview.net/forum?id=nkwPiBSw1f)] - Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning. [[PUB](https://openreview.net/forum?id=DUFD6vsyF8)] - Taming the Long Tail in Human Mobility Prediction. [[PUB](https://openreview.net/forum?id=wT2TIfHKp8)] - Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=EVw8Jh5Et9)] - Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution. [[PUB](https://openreview.net/forum?id=55zLbH7dE1)] - DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting. [[PUB](https://openreview.net/forum?id=FDfrPugkGU)] - Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability. [[PUB](https://openreview.net/forum?id=DLNOBJa7TM)] - Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. [[PUB](https://openreview.net/forum?id=FqWyzyErVT)] - FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction. [[PUB](https://openreview.net/forum?id=bMbteQRhDI)] - Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. [[PUB](https://openreview.net/forum?id=nw6ANsC66G)] - FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. [[PUB](https://openreview.net/forum?id=TcCorXxNJQ)] [[CODE](https://github.com/ATP-1010/FederatedLLM)] - Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains. [[PUB](https://openreview.net/forum?id=6SRPizFuaE)] - pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=xW6ga9i4eA)] - Why Go Full? Elevating Federated Learning Through Partial Network Updates. [[PUB](https://openreview.net/forum?id=6OK8Qy9yVu)] - FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. [[PUB](https://openreview.net/forum?id=E7fZOoiEKl)] - FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference. [[PUB](https://openreview.net/forum?id=I96GFYalFO)] - Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. [[PUB](https://openreview.net/forum?id=3YIyB82rjX)] - A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs. [[PUB](https://openreview.net/forum?id=h1iMVi2iEM)] - Private and Personalized Frequency Estimation in a Federated Setting. [[PUB](https://openreview.net/forum?id=0nzKznCjFG)] - The Sample-Communication Complexity Trade-off in Federated Q-Learning. [[PUB](https://openreview.net/forum?id=6YIpvnkjUK)] - Federated Ensemble-Directed Offline Reinforcement Learning. [[PUB](https://openreview.net/forum?id=ypaqE8UwsC)] - Federated Black-Box Adaptation for Semantic Segmentation. [[PUB](https://openreview.net/forum?id=Fp3JVz5XE7)] - Thinking Forward: Memory-Efficient Federated Finetuning of Language Models. [[PUB](https://openreview.net/forum?id=dGQtja9X2C)] [[CODE](https://github.com/Astuary/Spry)] - Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. [[PUB](https://openreview.net/forum?id=Y4L8GQXZZO)] - Optimal Design for Human Preference Elicitation. [[PUB](https://openreview.net/forum?id=cCGWj61Ael)] - Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. [[PUB](https://openreview.net/forum?id=y6JotynERr)] - Personalized Federated Learning via Feature Distribution Adaptation. [[PUB](https://openreview.net/forum?id=Wl2optQcng)] - SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. [[PUB](https://openreview.net/forum?id=HeJ1cBAgiV)] - A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings. [[PUB](https://openreview.net/forum?id=hilGwNabqB)] - RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation. [[PUB](https://openreview.net/forum?id=js74ZCddxG)] - FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. [[PUB](https://openreview.net/forum?id=QXkFC7D6p4)] - End-to-end Learnable Clustering for Intent Learning in Recommendation. [[PUB](https://openreview.net/forum?id=As91fJvY9E)] - FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation. [[PUB](https://openreview.net/forum?id=I3IuclVLFZ)] - Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting. [[PUB](https://openreview.net/forum?id=HS0faHRhWD)] - FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection. [[PUB](https://openreview.net/forum?id=D6MQrw9HFu)] [[CODE](https://github.com/XeniaLLL/FOOGD-main.git)] - A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm. [[PUB](https://openreview.net/forum?id=3YkeHuT1o6)] - FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction. [[PUB](https://openreview.net/forum?id=zBMKodNgKX)] - Low Precision Local Training is Enough for Federated Learning. [[PUB](https://openreview.net/forum?id=vvpewjtnvm)] [[CODE](https://github.com/digbangbang/LPT-FL)] - Resource-Aware Federated Self-Supervised Learning with Global Class Representations. [[PUB](https://openreview.net/forum?id=Of4iNAIUSe)] - On the Necessity of Collaboration for Online Model Selection with Decentralized Data. [[PUB](https://openreview.net/forum?id=uqWfLgZpV1)] - The Power of Extrapolation in Federated Learning. [[PUB](https://openreview.net/forum?id=FuTfZK7PK3)] - (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning. [[PUB](https://openreview.net/forum?id=lflwtGE6Vf)] - On Sampling Strategies for Spectral Model Sharding. [[PUB](https://openreview.net/forum?id=PgTHgLUFi3)] - Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. [[PUB](https://openreview.net/forum?id=isZ8XRe3De)] - SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead. [[PUB](https://openreview.net/forum?id=dAXuir2ets)] - HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning. [[PUB](https://openreview.net/forum?id=6LVxO1C819)] - Stabilized Proximal-Point Methods for Federated Optimization. [[PUB](https://openreview.net/forum?id=WukSyFSzDt)] - DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices. [[PUB](https://openreview.net/forum?id=Pezt0xttae)] - Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=g8wnC1E1OS)] - Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD. [[PUB](https://openreview.net/forum?id=j6Zsoj544N)] - FedAvP: Augment Local Data via Shared Policy in Federated Learning. [[PUB](https://openreview.net/forum?id=M1PRU0x1Iz)] - CoBo: Collaborative Learning via Bilevel Optimization. [[PUB](https://openreview.net/forum?id=SjQ1iIqpfU)] - Convergence Analysis of Split Federated Learning on Heterogeneous Data. [[PUB](https://openreview.net/forum?id=ud0RBkdBfE)] - Communication-Efficient Federated Group Distributionally Robust Optimization. [[PUB](https://openreview.net/forum?id=xNZEjFe0mh)] - Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity. [[PUB](https://openreview.net/forum?id=YxyYTcv3hp)] [[CODE](https://github.com/OngWinKent/Federated-Feature-Unlearning)] - Federated Learning over Connected Modes. [[PUB](https://openreview.net/forum?id=JL2eMCfDW8)] - Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning. [[PUB](https://openreview.net/forum?id=yvUHnBkCzd)] - Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors. [[PUB](https://openreview.net/forum?id=1kyc4TSOFZ)] - Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups. [[PUB](https://openreview.net/forum?id=T826pwZLci)] - DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans. [[PUB](https://openreview.net/forum?id=792txRlKit)] [[CODE](https://github.com/yuangan/DataStealing)] - Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning. [[PUB](https://openreview.net/forum?id=5FHzrRGOKR)] - Hierarchical Federated Learning with Multi-Timescale Gradient Correction. [[PUB](https://openreview.net/forum?id=aCAb1qNXI0)] - HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links. [[PUB](https://openreview.net/forum?id=3ie8NWA1El)] - SPEAR: Exact Gradient Inversion of Batches in Federated Learning. [[PUB](https://openreview.net/forum?id=lPDxPVS6ix)] - Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. [[PUB](https://openreview.net/forum?id=WftaVkL6G2)] - Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views. [[PUB](https://openreview.net/forum?id=GVlJVX3iiq)] [[CODE](https://github.com/5Martina5/FMCSC)] - Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data. [[PUB](https://openreview.net/forum?id=G89r8Mgi5r)] - Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=0LfgE6kvKZ)] - Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=MwJo3zuiTm)] - Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift. [[PUB](https://openreview.net/forum?id=6ejpSVIiIl)] [[CODE](https://github.com/Chen-Junbao/FedCCFA)] - Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning. [[PUB](https://openreview.net/forum?id=HhnpPISAUH)] - FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?. [[PUB](https://openreview.net/forum?id=JiRGxrqHh0)] - Active preference learning for ordering items in- and out-of-sample. [[PUB](https://openreview.net/forum?id=PSLH5q7PFo)] - Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources. [[PUB](https://openreview.net/forum?id=gkOzoHBXUw)] - Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. [[PUB](https://openreview.net/forum?id=WBLPlszJI5)] - Revisiting Ensembling in One-Shot Federated Learning. [[PUB](https://openreview.net/forum?id=7rWTS2wuYX)] - FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models. [[PUB](https://openreview.net/forum?id=djGx0hucok)] - $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [[PUB](https://openreview.net/forum?id=I79q7wIRkS)] - FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection. [[PUB](https://openreview.net/forum?id=rovpCs3ZEO)] - pfl-research: simulation framework for accelerating research in Private Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/4c8c6de56ecdd05e61abcd9e057c6142-Abstract-Datasets_and_Benchmarks_Track.html)] - $C2M3$: Cycle-Consistent Multi-Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/3268f1e2474ef9d1af7f034401197a7f-Abstract-Conference.html)] - A Kernel Perspective on Distillation-based Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/a71c1931d3fb8ba564f7458d0657d0b1-Abstract-Conference.html)] - Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/013f9cd52b38e3e53475605d2b8e7c23-Abstract-Conference.html)] [[CODE](https://github.com/bigdata-ustc/Coral)] - Collaborative Refining for Learning from Inaccurate Labels. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/a8809ae67a7aad49a64d615468d72808-Abstract-Conference.html)] - Communication Efficient Distributed Training with Distributed Lion. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/20cea6c1b36ae5f69c48427a68b67fbc-Abstract-Conference.html)] - DAGER: Exact Gradient Inversion for Large Language Models. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/9ff1577a1f8308df1ccea6b4f64a103f-Abstract-Conference.html)] - EMR-Merging: Tuning-Free High-Performance Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/dda5cac5272a9bcd4bc73d90bc725ef1-Abstract-Conference.html)] - Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/d8a6eb79f8ccaacbe7198a5caf3a0323-Abstract-Conference.html)] - Gradient-free Decoder Inversion in Latent Diffusion Models. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/970f59b22f4c72aec75174aae63c7459-Abstract-Conference.html)] - Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/b041cbfcc3f282a9b3c8eb9c16177529-Abstract-Conference.html)] - Parameter Competition Balancing for Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/99fc8bc48b917c301a80cb74d91c0c06-Abstract-Conference.html)] - SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/a97b58c4f7551053b0512f92244b0810-Abstract-Conference.html)] - Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/8fcd17eb91bae20d9826786d7d6be799-Abstract-Conference.html)] - Unravelling in Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/b0499a1aecf036d42074d03f621d7864-Abstract-Conference.html)] #### NeurIPS workshop - Momentum Approximation in Asynchronous Private Federated Learning. [[PUB](https://openreview.net/forum?id=pEpjKicxFk)] - Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning. [[PUB](https://openreview.net/forum?id=8TrYvsbw1f)] - Federated Learning with Generative Content. [[PUB](https://openreview.net/forum?id=hMbgXHjWrg)] - Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models. [[PUB](https://openreview.net/forum?id=pxP2M3xiE6)] - Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [[PUB](https://openreview.net/forum?id=1JGa1OIRjQ)] - Defection-Free Collaboration between Competitors in a Learning System. [[PUB](https://openreview.net/forum?id=2Sd5xNv1sm)] - On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments. [[PUB](https://openreview.net/forum?id=Eph8dS188u)] - EncCluster: Bringing Functional Encryption in Federated Foundational Models. [[PUB](https://openreview.net/forum?id=7bgJ7t5kkW)] - Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [[PUB](https://openreview.net/forum?id=SXMsg44Znz)] - Hot Pluggable Federated Learning. [[PUB](https://openreview.net/forum?id=FazIrAXoM6)] - Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees. [[PUB](https://openreview.net/forum?id=MxgmAil8ud)] - The Future of Large Language Model Pre-training is Federated. [[PUB](https://openreview.net/forum?id=hfeH5AP9NY)] - Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms. [[PUB](https://openreview.net/forum?id=jNZEIQsJes)] - The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain. [[PUB](https://openreview.net/forum?id=RoUUV2wLdn)] - ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=zpEQUbYZPc)] - DeComFL: Federated Learning with Dimension-Free Communication. [[PUB](https://openreview.net/forum?id=Vy9ltlTXXd)] - Improving Group Connectivity for Generalization of Federated Deep Learning. [[PUB](https://openreview.net/forum?id=vGyB8PVl4C)] - MAP: Model Merging with Amortized Pareto Front Using Limited Computation. [[PUB](https://openreview.net/forum?id=KfOdVp4pfm)] - OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning. [[PUB](https://openreview.net/forum?id=qQdPSuW7qx)] - Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration. [[PUB](https://openreview.net/forum?id=OxpWu6J0TW)] - Worldwide Federated Training of Language Models. [[PUB](https://openreview.net/forum?id=YMSLZUmQVV)] - FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator. [[PUB](https://openreview.net/forum?id=uBooD9HQQu)] - Enhancing Causal Discovery in Federated Settings with Limited Local Samples. [[PUB](https://openreview.net/forum?id=Js64okXDUE)] - $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [[PUB](https://openreview.net/forum?id=6WNNB9TaVw)] - DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing. [[PUB](https://openreview.net/forum?id=GdzTE7eruH)] #### JMLR - FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. [[PUB](https://jmlr.org/papers/v25/23-0764.html)] - A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness. [[PUB](https://jmlr.org/papers/v25/23-0215.html)] - Compressed and distributed least-squares regression: convergence rates with applications to federated learning. [[PUB](https://jmlr.org/papers/v25/23-1040.html)] - Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique. [[PUB](https://jmlr.org/papers/v25/24-1189.html)] - Federated Automatic Differentiation. [[PUB](https://jmlr.org/papers/v25/23-0223.html)] - Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning. [[PUB](https://jmlr.org/papers/v25/22-1036.html)] - Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms. [[PUB](https://jmlr.org/papers/v25/21-0316.html)] #### ICML - Effective Federated Graph Matching. [[PUB](https://openreview.net/forum?id=rSfzchjIYu)] - Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation. [[PUB](https://openreview.net/forum?id=zwUEk9WpsR)] - Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients. [[PUB](https://openreview.net/forum?id=2zLt2Odckx)] - FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models. [[PUB](https://openreview.net/forum?id=AoYhtJ4A90)] - Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning. [[PUB](https://openreview.net/forum?id=p0MGN0LSnx)] - A New Theoretical Perspective on Data Heterogeneity in Federated Optimization. [[PUB](https://openreview.net/forum?id=re6es2atbl)] - Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models. [[](https://openreview.net/forum?id=QgvBcOsF4B)] - Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments. [[PUB](https://openreview.net/forum?id=g43yUNWX4V)] - Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. [[PUB](https://openreview.net/forum?id=Izv7gBnap3)] - Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. [[PUB](https://openreview.net/forum?id=yHRxnhKyEJ)] - Accelerating Federated Learning with Quick Distributed Mean Estimation. [[PUB](https://openreview.net/forum?id=gWEwIlZrbQ)] - Fair Federated Learning via the Proportional Veto Core. [[PUB](https://openreview.net/forum?id=6Zgjrowepn)] - AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning. [[PUB](https://openreview.net/forum?id=PHUAG63Efe)] [[CODE](https://github.com/MIC-DKFZ/deki-smpc)] - Recovering Labels from Local Updates in Federated Learning. [[PUB](https://openreview.net/forum?id=E41gvBG4s6)] - FedMBridge: Bridgeable Multimodal Federated Learning. [[PUB](https://openreview.net/forum?id=jrHUbftLd6)] - Harmonizing Generalization and Personalization in Federated Prompt Learning. [[PUB](https://openreview.net/forum?id=YYwERRXsJW)] - Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. [[PUB](https://openreview.net/forum?id=6axTFAlzRV)] - Accelerating Heterogeneous Federated Learning with Closed-form Classifiers. [[PUB](https://openreview.net/forum?id=cMige5MK1N)] - Federated Combinatorial Multi-Agent Multi-Armed Bandits. [[PUB](https://openreview.net/forum?id=lrFwPeDdEQ)] - A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization. [[PUB](https://openreview.net/forum?id=GentO2E4ID)] - Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. [[PUB](https://openreview.net/forum?id=sSAEhcdB9N)] - FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering. [[PUB](https://openreview.net/forum?id=kc4dZYJlJG)] - Pursuing Overall Welfare in Federated Learning through Sequential Decision Making. [[PUB](https://openreview.net/forum?id=foPMkomvk1)] [[CODE](https://github.com/vaseline555/AAggFF)] - PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. [[PUB](https://openreview.net/forum?id=3WCvnkHnxV)] [[CODE](https://github.com/houcharlie/PrE-Text)] - Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=k2axqNsVVO)] - Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors. [[PUB](https://openreview.net/forum?id=mNzkumTSVL)] - Federated Optimization with Doubly Regularized Drift Correction. [[PUB](https://openreview.net/forum?id=JD03zxWZzs)] - FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data. [[PUB](https://openreview.net/forum?id=0nMzOmkBHC)] - Certifiably Byzantine-Robust Federated Conformal Prediction. [[PUB](https://openreview.net/forum?id=4axAQHwBOE)] - Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning. [[PUB](https://openreview.net/forum?id=vQmVmMN5ft)] - Clustered Federated Learning via Gradient-based Partitioning. [[PUB](https://openreview.net/forum?id=J4HJUF70qm)] - Recurrent Early Exits for Federated Learning with Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=w4B42sxNq3)] - Rethinking the Flat Minima Searching in Federated Learning. [[PUB](https://openreview.net/forum?id=6TM62kpI5c)] - FedBAT: Communication-Efficient Federated Learning via Learnable Binarization. [[PUB](https://openreview.net/forum?id=x2zxPwCkAZ)] - Federated Representation Learning in the Under-Parameterized Regime. [[PUB](https://openreview.net/forum?id=LIQYhV45D4)] - FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees. [[PUB](https://openreview.net/forum?id=akyElNlUVA)] - Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. [[PUB](https://openreview.net/forum?id=wuQ2DRPAuy)] - SILVER: Single-loop variance reduction and application to federated learning. [[PUB](https://openreview.net/forum?id=pOgMluzEIH)] - SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding. [[PUB](https://openreview.net/forum?id=zEqeNEuiJr)] - FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler. [[PUB](https://openreview.net/forum?id=XecUTmB9yD)] - Federated Continual Learning via Prompt-based Dual Knowledge Transfer. [[PUB](https://openreview.net/forum?id=Kqa5JakTjB)] - Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes. [[PUB](https://openreview.net/forum?id=cit0hg4sEz)] - Decomposable Submodular Maximization in Federated Setting. [[PUB](https://openreview.net/forum?id=SAbZExIIgG)] - Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. [[PUB](https://openreview.net/forum?id=sTVSyqD6XX)] - Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials. [[PUB](https://openreview.net/forum?id=01M0N8VgfB)] - Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!. [[PUB](https://openreview.net/forum?id=ffS0aYP6mk)] - Byzantine Resilient and Fast Federated Few-Shot Learning. [[PUB](https://openreview.net/forum?id=q5q59s2WJy)] - Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization. [[PUB](https://openreview.net/forum?id=Kbd9A4lVoX)] - Ranking-based Client Imitation Selection for Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=FMEhnS0948)] - Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. [[PUB](https://openreview.net/forum?id=kVgpa1rfLO)] - FADAS: Towards Federated Adaptive Asynchronous Optimization. [[PUB](https://openreview.net/forum?id=j56JAd29uH)] - Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices. [[PUB](https://openreview.net/forum?id=LIPGadocTe)] - FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error. [[PUB](https://openreview.net/forum?id=Wjq2bS7fTK)] - MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis. [[PUB](https://openreview.net/forum?id=Jvh8HM9YEJ)] - Federated Neuro-Symbolic Learning. [[PUB](https://openreview.net/forum?id=EQXZqBXeW9)] - Adaptive Group Personalization for Federated Mutual Transfer Learning. [[PUB](https://openreview.net/forum?id=DqC9XiI71U)] - Balancing Similarity and Complementarity for Federated Learning. [[PUB](https://openreview.net/forum?id=v6tAdeCXKH)] - Federated Self-Explaining GNNs with Anti-shortcut Augmentations. [[PUB](https://openreview.net/forum?id=ZxDqSBgFSM)] - A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization. [[PUB](https://openreview.net/forum?id=NkN6wrYXe5)] - COALA: A Practical and Vision-Centric Federated Learning Platform. [[PUB](https://openreview.net/forum?id=ATRnM8PyQX)] [[CODE](https://github.com/SonyResearch/COALA)] - Collaborative Learning with Different Labeling Functions. [[PUB](https://proceedings.mlr.press/v235/deng24d.html)] - EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting. [[PUB](https://proceedings.mlr.press/v235/wang24w.html)] - Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation. [[PUB](https://proceedings.mlr.press/v235/pu24a.html)] - Localizing Task Information for Improved Model Merging and Compression. [[PUB](https://proceedings.mlr.press/v235/wang24k.html)] - Merging Multi-Task Models via Weight-Ensembling Mixture of Experts. [[PUB](https://proceedings.mlr.press/v235/tang24e.html)] - Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering. [[PUB](https://proceedings.mlr.press/v235/li24cq.html)] - Representation Surgery for Multi-Task Model Merging. [[PUB](https://proceedings.mlr.press/v235/yang24t.html)] - Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration. [[PUB](https://proceedings.mlr.press/v235/yao24d.html)] - Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models. [[PUB](https://proceedings.mlr.press/v235/mergny24a.html)] #### Mach Learn - Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [[PUB](https://link.springer.com/article/10.1007/s10994-024-06541-y)] - Communication-efficient clustered federated learning via model distance. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06443-5)] - Federated learning with superquantile aggregation for heterogeneous data. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06332-x)] [[PDF](https://arxiv.org/abs/2112.09429)] [[CODE](https://github.com/krishnap25/simplicial-fl)] - Aligning model outputs for class imbalanced non-IID federated learning. [[PUB](https://link.springer.com/article/10.1007/s10994-022-06241-5)] #### TPAMI - Federated Learning of Generalized Linear Causal Networks. [[PUB](https://ieeexplore.ieee.org/document/10480288)] - Cross-Modal Federated Human Activity Recognition. [[PUB](https://ieeexplore.ieee.org/document/10440498)] - Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling. [[PUB](https://ieeexplore.ieee.org/document/10402074)] [[PDF](https://arxiv.org/abs/2111.14008)] [[CODE](https://github.com/UMDataScienceLab/Federated_Gaussian_Process)] - The Impact of Adversarial Attacks on Federated Learning: A Survey. [[PUB](https://ieeexplore.ieee.org/document/10274102)] - Understanding and Mitigating Dimensional Collapse in Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10336535)] [[PDF](https://arxiv.org/abs/2210.00226)] [[CODE](https://github.com/bytedance/FedDecorr)] - No One Left Behind: Real-World Federated Class-Incremental Learning. [[PUB](https://ieeexplore.ieee.org/document/10323204)] [[PDF](https://arxiv.org/abs/2302.00903)] [[CODE](https://github.com/JiahuaDong/LGA)] - Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning. [[PUB](https://ieeexplore.ieee.org/document/10295990)] [[PDF](https://arxiv.org/abs/2309.16286)] [[CODE](https://github.com/WenkeHuang/FCCL)] - Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy. [[PUB](https://ieeexplore.ieee.org/document/10316599)] [[PDF](https://arxiv.org/abs/1912.07902)] [[CODE](https://github.com/IoTDATALab/MAPA)] - A Bayesian Federated Learning Framework With Online Laplace Approximation. [[PUB](https://ieeexplore.ieee.org/document/10274722)] [[PDF](https://arxiv.org/abs/2102.01936)] [[CODE](https://github.com/Klitter/A-Bayesian-Federated-Learning-Framework-with-Online-Laplace-Approximation)] - Federated Feature Augmentation and Alignment. [[PUB](https://doi.org/10.1109/TPAMI.2024.3457751)] - Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark. [[PUB](https://doi.org/10.1109/TPAMI.2024.3418862)] - Gradient Inversion Attacks: Impact Factors Analyses and Privacy Enhancement. [[PUB](https://doi.org/10.1109/TPAMI.2024.3430533)] - Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification. [[PUB](https://doi.org/10.1109/TPAMI.2023.3334741)] - Improved Diversity-Promoting Collaborative Metric Learning for Recommendation. [[PUB](https://doi.org/10.1109/TPAMI.2024.3412687)] #### ICLR - Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. [[PUB](https://openreview.net/forum?id=tm8s3696Ox)] - One-shot Empirical Privacy Estimation for Federated Learning. [[PUB](https://openreview.net/forum?id=0BqyZSWfzo)] [[PDF](https://arxiv.org/abs/2302.03098)] - Stochastic Controlled Averaging for Federated Learning with Communication Compression. [[PUB](https://openreview.net/forum?id=jj5ZjZsWJe)] [[PDF](https://arxiv.org/abs/2308.08165)] - A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging. [[PUB](https://openreview.net/forum?id=ZKEuFKfCKA)] [[PDF](https://arxiv.org/abs/2306.03401)] [[CODE](https://github.com/IBM/fedau)] - A Mutual Information Perspective on Federated Contrastive Learning. [[PUB](https://openreview.net/forum?id=JrmPG9ufKg)] - Benchmarking Algorithms for Federated Domain Generalization. [[PUB](https://openreview.net/forum?id=wprSv7ichW)] [[PDF](https://arxiv.org/abs/2307.04942)] [[CODE](https://github.com/inouye-lab/FedDG_Benchmark)] - Effective and Efficient Federated Tree Learning on Hybrid Data. [[PUB](https://openreview.net/forum?id=py4ZV2qYQI)] [[PDF](https://arxiv.org/abs/2310.11865)] - Federated Recommendation with Additive Personalization. [[PUB](https://openreview.net/forum?id=xkXdE81mOK)] [[PDF](https://arxiv.org/abs/2301.09109)] [[CODE](https://github.com/mtics/FedRAP)] - Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration. [[PUB](https://openreview.net/forum?id=4aywmeb97I)] [[SUPP](https://openreview.net/attachment?id=4aywmeb97I&name=supplementary_material)] - Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning. [[PUB](https://openreview.net/forum?id=nAs4LdaP9Y)] [[SUPP](https://openreview.net/attachment?id=nAs4LdaP9Y&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.01289)] - Accurate Forgetting for Heterogeneous Federated Continual Learning. [[PUB](https://openreview.net/forum?id=ShQrnAsbPI)] [[CODE](https://anonymous.4open.science/r/AF-FCL-7D65)] - Federated Causal Discovery from Heterogeneous Data. [[PUB](https://openreview.net/forum?id=m7tJxajC3G)] [[PDF](https://arxiv.org/abs/2402.13241)] [[CODE](https://github.com/lokali/FedCDH)] - On Differentially Private Federated Linear Contextual Bandits. [[PUB](https://openreview.net/forum?id=cuAxSHcsSX)] [[SUPP](https://openreview.net/attachment?id=cuAxSHcsSX&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.13945)] - Incentivized Truthful Communication for Federated Bandits. [[PUB](https://openreview.net/forum?id=ykEixGIJYb)] [[PDF](https://arxiv.org/abs/2402.04485)] - Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting. [[PUB](https://openreview.net/forum?id=6J3ehSUrMU)] - FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. [[PUB](https://openreview.net/forum?id=hbHwZYqk9T)] - Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [[PUB](https://openreview.net/forum?id=NW31gAylIm)] [[PDF](https://arxiv.org/abs/2310.06123)] - Improving LoRA in Privacy-preserving Federated Learning. [[PUB](https://openreview.net/forum?id=NLPzL6HWNl)] - FedWon: Triumphing Multi-domain Federated Learning Without Normalization. [[PUB](https://openreview.net/forum?id=hAYHmV1gM8)] [[PDF](https://arxiv.org/abs/2306.05879)] - FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning. [[PUB](https://openreview.net/forum?id=DRu8PMHgCh)] - FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler. [[PUB](https://openreview.net/forum?id=msXxrttLOi)] [[PDF](https://arxiv.org/abs/2309.14675)] [[CODE](https://github.com/APPFL/FedCompass)] [[PAGE](https://appfl.github.io/FedCompass)] - Bayesian Coreset Optimization for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=uz7d2N2zul)] - Layer-wise linear mode connectivity. [[PUB](https://openreview.net/forum?id=LfmZh91tDI)] [[PDF](https://arxiv.org/abs/2307.06966)] [[SUPP](https://openreview.net/attachment?id=LfmZh91tDI&name=supplementary_material)] - Fake It Till Make It: Federated Learning with Consensus-Oriented Generation. [[PUB](https://openreview.net/forum?id=NY3wMJuaLf)] [[PDF](https://arxiv.org/abs/2312.05966)] - Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=krx55l2A6G)] [[SUPP](https://openreview.net/attachment?id=krx55l2A6G&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.03013)] - Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning. [[PUB](https://openreview.net/forum?id=D2eOVqPX9g)] [[PDF](https://arxiv.org/abs/2401.15273)] - Adaptive Federated Learning with Auto-Tuned Clients. [[PUB](https://openreview.net/forum?id=g0mlwqs8pi)] [[SUPP](https://openreview.net/attachment?id=g0mlwqs8pi&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.11201)] - Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. [[PUB](https://openreview.net/forum?id=AJBGSVSTT2)] [[SUPP](https://openreview.net/attachment?id=AJBGSVSTT2&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2308.04466)] - Federated Q-Learning: Linear Regret Speedup with Low Communication Cost. [[PUB](https://openreview.net/forum?id=fe6ANBxcKM)] [[SUPP](https://openreview.net/attachment?id=fe6ANBxcKM&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2312.15023)] - FedImpro: Measuring and Improving Client Update in Federated Learning. [[PUB](https://openreview.net/forum?id=giU9fYGTND)] [[PDF](https://arxiv.org/abs/2402.07011)] - Federated Wasserstein Distance. [[PUB](https://openreview.net/forum?id=rsg1mvUahT)] [[SUPP](https://openreview.net/attachment?id=rsg1mvUahT&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.01973)] - An improved analysis of per-sample and per-update clipping in federated learning. [[PUB](https://openreview.net/forum?id=BdPvGRvoBC)] - FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation. [[PUB](https://openreview.net/forum?id=nbPGqeH3lt)] [[SUPP](https://openreview.net/attachment?id=nbPGqeH3lt&name=supplementary_material)] - Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning. [[PUB](https://openreview.net/forum?id=Cc0qk6r4Nd)] [[PDF](https://arxiv.org/abs/2308.11464)] - Momentum Benefits Non-iid Federated Learning Simply and Provably. [[PUB](https://openreview.net/forum?id=TdhkAcXkRi)] [[PDF](https://arxiv.org/abs/2306.16504)] - Communication-Efficient Federated Non-Linear Bandit Optimization. [[PUB](https://openreview.net/forum?id=nFI3wFM9yN)] [[PDF](https://arxiv.org/abs/2311.01695)] - Fair and Efficient Contribution Valuation for Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=sLQb8q0sUi)] [[SUPP](https://openreview.net/attachment?id=sLQb8q0sUi&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2201.02658)] [[CODE](https://github.com/zhenanf/VerFedLogistic.jl)] - Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [[PUB](https://openreview.net/forum?id=SBj2Qdhgew)] [[PDF](https://arxiv.org/abs/2307.11333)] - Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients. [[PUB](https://openreview.net/forum?id=8FHWkY0SwF)] - PeFLL: Personalized Federated Learning by Learning to Learn. [[PUB](https://openreview.net/forum?id=MrYiwlDRQO)] [[SUPP](https://openreview.net/attachment?id=MrYiwlDRQO&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.05515)] - Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. [[PUB](https://openreview.net/forum?id=hORCalGn3Z)] [[SUPP](https://openreview.net/attachment?id=hORCalGn3Z&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.05100)] - FedInverse: Evaluating Privacy Leakage in Federated Learning. [[PUB](https://openreview.net/forum?id=nTNgkEIfeb)] [[SUPP](https://openreview.net/attachment?id=nTNgkEIfeb&name=supplementary_material)] - FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization. [[PUB](https://openreview.net/forum?id=kjn99xFUF3)] [[SUPP](https://openreview.net/attachment?id=kjn99xFUF3&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.06103)] - Robust Training of Federated Models with Extremely Label Deficiency. [[PUB](https://openreview.net/forum?id=qxLVaYbsSI)] [[PDF](https://arxiv.org/abs/2402.14430)] [[CODE](https://github.com/visitworld123/Twin-sight)] - Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. [[PUB](https://openreview.net/forum?id=EcetCr4trp)] - Teach LLMs to Phish: Stealing Private Information from Language Models. [[PUB](https://openreview.net/forum?id=qo21ZlfNu6)] - Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix. [[PUB](https://openreview.net/forum?id=rM9VJPB20F)] - Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. [[PUB](https://openreview.net/forum?id=CIqjp9yTDq)] [[PDF](https://arxiv.org/abs/2312.14567)] - Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks. [[PUB](https://openreview.net/forum?id=s8cMuxI5gu)] [[SUPP](https://openreview.net/attachment?id=s8cMuxI5gu&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2402.03124)] [[CODE](https://github.com/ybwang119/label_recovery)] - Local Composite Saddle Point Optimization. [[PUB](https://openreview.net/forum?id=kklwv4c4dI)] [[PDF](https://arxiv.org/abs/2305.15643)] - Enhancing Neural Training via a Correlated Dynamics Model. [[PUB](https://openreview.net/forum?id=c9xsaASm9L)] [[PDF](https://arxiv.org/abs/2312.13247)] - EControl: Fast Distributed Optimization with Compression and Error Control. [[PUB](https://openreview.net/forum?id=lsvlvWB9vz)] [[SUPP](https://openreview.net/attachment?id=lsvlvWB9vz&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.05645)] - Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit. [[PUB](https://openreview.net/forum?id=m52uU0dVbH)] - FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent. [[PUB](https://openreview.net/forum?id=Kl9CqKf7h6)] [[SUPP](https://openreview.net/attachment?id=Kl9CqKf7h6&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.03156)] [[CODE](https://github.com/ATP-1010/FedHyper)] - Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate. [[PUB](https://openreview.net/forum?id=7pWRLDBAtc)] - Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [[PUB](https://openreview.net/forum?id=zzqn5G9fjn)] - Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity. [[PUB](https://openreview.net/forum?id=1ii8idH4tH)] - VFLAIR: A Research Library and Benchmark for Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=sqRgz88TM3)] [[PDF](https://arxiv.org/abs/2310.09827)] [[CODE](https://github.com/FLAIR-THU/VFLAIR)] - Incentive-Aware Federated Learning with Training-Time Model Rewards. [[PUB](https://openreview.net/forum?id=FlY7WQ2hWS)] [[SUPP](https://openreview.net/attachment?id=FlY7WQ2hWS&name=supplementary_material)] - VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks. [[PUB](https://openreview.net/forum?id=glwwbaeKm2)] [[PDF](https://arxiv.org/abs/2307.02040)] [[CODE](https://github.com/Xtra-Computing/VertiBench)] - FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data. [[PUB](https://openreview.net/forum?id=V3j5d0GQgH)] [[SUPP](https://openreview.net/attachment?id=V3j5d0GQgH&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2401.08977)] - Demystifying Local &amp; Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [[PUB](https://openreview.net/forum?id=SBj2Qdhgew)] - Federated Text-driven Prompt Generation for Vision-Language Models. [[PUB](https://openreview.net/forum?id=NW31gAylIm)] - A Good Learner can Teach Better: Teacher-Student Collaborative Knowledge Distillation. [[PUB](https://openreview.net/forum?id=Ixi4j6LtdX)] - AdaMerging: Adaptive Model Merging for Multi-Task Learning. [[PUB](https://openreview.net/forum?id=nZP6NgD3QY)] - CLAP: Collaborative Adaptation for Patchwork Learning. [[PUB](https://openreview.net/forum?id=8EyRkd3Qj2)] - CO2: Efficient Distributed Training with Full Communication-Computation Overlap. [[PUB](https://openreview.net/forum?id=ZO5cn4IfaN)] - Model Merging by Uncertainty-Based Gradient Matching. [[PUB](https://openreview.net/forum?id=D7KJmfEDQP)] - ZipIt! Merging Models from Different Tasks without Training. [[PUB](https://openreview.net/forum?id=LEYUkvdUhq)] ### 2023 #### machine learning - Ensemble and continual federated learning for classification tasks. [[PUB](https://doi.org/10.1007/s10994-023-06330-z)] - FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [[PUB](https://doi.org/10.1007/s10994-023-06360-7)] - Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [[PUB](https://doi.org/10.1007/s10994-022-06292-8)] #### NeurIPS - SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning. [[PUB](https://openreview.net/forum?id=ZdxGmJGKOo)] [[PDF](https://arxiv.org/abs/2305.19442)] [[SUPP](https://openreview.net/attachment?id=ZdxGmJGKOo&name=supplementary_material)] - Mechanism Design for Collaborative Normal Mean Estimation. [[PUB](https://openreview.net/forum?id=yKCLfOOIL7)] [[PDF](https://arxiv.org/abs/2306.06351)] - Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. [[PUB](https://openreview.net/forum?id=n3fPDW87is)] [[PDF](https://arxiv.org/abs/2309.13591)] [[CODE](https://github.com/GeovaniRizk/Robust-Distributed-Learning-Tight-Error-Bounds-and-Breakdown-Point-under-Data-Heterogeneity)] - Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization. [[PUB](https://openreview.net/forum?id=9OqezkNxnX)] [[SUPP](https://openreview.net/attachment?id=9OqezkNxnX&name=supplementary_material)] - Convergence Analysis of Sequential Federated Learning on Heterogeneous Data. [[PUB](https://openreview.net/forum?id=Dxhv8Oja2V)] [[PDF](https://arxiv.org/abs/2311.03154)] [[CODE](https://github.com/liyipeng00/convergence)] - Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. [[PUB](https://openreview.net/forum?id=LGKxz9clGG)] [[PDF](https://arxiv.org/abs/2310.15165)] [[CODE](https://github.com/sarapieri/fed_het.git)] - Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. [[PUB](https://openreview.net/forum?id=rzDBoh1tBh)] [[SUPP](https://openreview.net/attachment?id=rzDBoh1tBh&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.09396)] - Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. [[PUB](https://openreview.net/forum?id=46x3zvYCyQ)] [[SUPP](https://openreview.net/attachment?id=46x3zvYCyQ&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.13024)] - Incentivized Communication for Federated Bandits. [[PUB](https://openreview.net/forum?id=1aQivXgZKj)] [[PDF](https://arxiv.org/abs/2309.11702)] - Multiply Robust Federated Estimation of Targeted Average Treatment Effects. [[PUB](https://openreview.net/forum?id=M6UccKMFGl)] [[PDF](https://arxiv.org/abs/2309.12600)] - IBA: Towards Irreversible Backdoor Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=cemEOP8YoC)] [[SUPP](https://openreview.net/attachment?id=cemEOP8YoC&name=supplementary_material)] [[CODE](https://github.com/sail-research/iba)] - EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=P3Z59Okb5I)] [[SUPP](https://openreview.net/attachment?id=P3Z59Okb5I&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.07485)] - Federated Linear Bandits with Finite Adversarial Actions. [[PUB](https://openreview.net/forum?id=bzXpQUnule)] [[SUPP](https://openreview.net/attachment?id=bzXpQUnule&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.00973)] - FedNAR: Federated Optimization with Normalized Annealing Regularization. [[PUB](https://openreview.net/forum?id=x5fs7TXKDc)] [[SUPP](https://openreview.net/attachment?id=x5fs7TXKDc&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.03163)] [[CODE](https://github.com/ljb121002/fednar)] - Guiding The Last Layer in Federated Learning with Pre-Trained Models. [[PUB](https://openreview.net/forum?id=HRGd5dcVfw)] [[SUPP](https://openreview.net/attachment?id=HRGd5dcVfw&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.03937)] [[CODE](https://github.com/GwenLegate/GuidingLastLayerFLPretrain)] - Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. [[PUB](https://openreview.net/forum?id=0ycX03sMAT)] [[SUPP](https://openreview.net/attachment?id=0ycX03sMAT&name=supplementary_material)] - Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection. [[PUB](https://openreview.net/forum?id=2D7ou48q0E)] [[SUPP](https://openreview.net/attachment?id=2D7ou48q0E&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.17097)] [[CODE](https://github.com/Kthyeon/ssfod)] - A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks. [[PUB](https://openreview.net/forum?id=3b9sqxCW1x)] [[PDF](https://arxiv.org/abs/2311.07784)] [[CODE](https://github.com/SaraBabakN/MFCL-NeurIPS23)] - Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning. [[PUB](https://openreview.net/forum?id=qJJmu4qsLO)] [[SUPP](https://openreview.net/attachment?id=qJJmu4qsLO&name=supplementary_material)] - One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. [[PUB](https://openreview.net/forum?id=KMxRQO7P98)] [[SUPP](https://openreview.net/attachment?id=KMxRQO7P98&name=supplementary_material)] [[CODE](https://github.com/lzcemma/RACE_Distance)] - Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training. [[PUB](https://openreview.net/forum?id=V5cQH7JbGo)] [[SUPP](https://openreview.net/attachment?id=V5cQH7JbGo&name=supplementary_material)] [[CODE](https://github.com/git-disl/Lockdown)] - FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=nX0zYBGEka)] [[SUPP](https://openreview.net/attachment?id=nX0zYBGEka&name=supplementary_material)] [[CODE](https://github.com/AI-secure/FedGame)] - Towards Personalized Federated Learning via Heterogeneous Model Reassembly. [[PUB](https://openreview.net/forum?id=zpVCITHknd)] [[SUPP](https://openreview.net/attachment?id=zpVCITHknd&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2308.08643)] [[CODE](https://github.com/JackqqWang/pfedHR)] - Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction. [[PUB](https://openreview.net/forum?id=AWpWaub6nf)] [[SUPP](https://openreview.net/attachment?id=AWpWaub6nf&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.08670)] - DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=3H9QH1v6U9)] [[SUPP](https://openreview.net/attachment?id=3H9QH1v6U9&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.13546)] [[CODE](https://anonymous.4open.science/r/DFRD-0C83/)] - A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=AYiRHZirD2)] [[SUPP](https://openreview.net/attachment?id=AYiRHZirD2&name=supplementary_material)] [[CODE](https://github.com/GanyuWang/VFL-CZOFO)] - RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks. [[PUB](https://openreview.net/forum?id=3n8PNUdvSg)] [[SUPP](https://openreview.net/attachment?id=3n8PNUdvSg&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.05431)] - Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. [[PUB](https://openreview.net/forum?id=wwmKVO8bsR)] [[SUPP](https://openreview.net/attachment?id=wwmKVO8bsR&name=supplementary_material)] [[CODE](https://github.com/MediaBrain-SJTU/FedGELA.git)] - Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. [[PUB](https://openreview.net/forum?id=Yq6GKgN3RC)] [[SUPP](https://openreview.net/attachment?id=Yq6GKgN3RC&name=supplementary_material)] [[CODE](https://github.com/MingruiLiu-ML-Lab/episode_plusplus)] - FedL2P: Federated Learning to Personalize. [[PUB](https://openreview.net/forum?id=FM81CI68Iz)] [[SUPP](https://openreview.net/attachment?id=FM81CI68Iz&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.02420)] [[CODE](https://github.com/royson/fedl2p/)] - Adaptive Test-Time Personalization for Federated Learning. [[PUB](https://openreview.net/forum?id=rbw9xCU6Ci)] [[PDF](https://arxiv.org/abs/2310.18816)] [[CODE](https://github.com/baowenxuan/ATP)] - Federated Conditional Stochastic Optimization. [[PUB](https://openreview.net/forum?id=E0Gw1uz7lU)] [[SUPP](https://openreview.net/attachment?id=E0Gw1uz7lU&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.02524)] [[CODE](https://github.com/xidongwu/Federated-Minimax-and-Conditional-Stochastic-Optimization/tree/main)] - Federated Spectral Clustering via Secure Similarity Reconstruction. [[PUB](https://openreview.net/forum?id=RW7rZ8Y3Bp)] - Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM. [[PUB](https://openreview.net/forum?id=EcmqyXekuP)] [[SUPP](https://openreview.net/attachment?id=EcmqyXekuP&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2304.12534)] - FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks. [[PUB](https://openreview.net/forum?id=ody3RBUuJS)] [[SUPP](https://openreview.net/attachment?id=ody3RBUuJS&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2201.12433)] [[CODE](https://github.com/yh-yao/FedGCN)] - Federated Multi-Objective Learning. [[PUB](https://openreview.net/forum?id=OlSTwlz96r)] [[SUPP](https://openreview.net/attachment?id=OlSTwlz96r&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.09866)] - FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout. [[PUB](https://openreview.net/forum?id=rG1M3kOVba)] [[SUPP](https://openreview.net/attachment?id=rG1M3kOVba&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2307.02623)] [[CODE](https://github.com/iwang05/FLuID)] - Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning. [[PUB](https://openreview.net/forum?id=j4QVhftpYM)] [[SUPP](https://openreview.net/attachment?id=j4QVhftpYM&name=supplementary_material)] - Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. [[PUB](https://openreview.net/forum?id=B5XwENgy0T)] [[SUPP](https://openreview.net/attachment?id=B5XwENgy0T&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.06701)] - StableFDG: Style and Attention Based Learning for Federated Domain Generalization. [[PUB](https://openreview.net/forum?id=IjZa2fQ8tL)] [[PDF](https://arxiv.org/abs/2311.00227)] - Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization. [[PUB](https://openreview.net/forum?id=ylPX5D7It7)] [[SUPP](https://openreview.net/attachment?id=ylPX5D7It7&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.05706)] - DELTA: Diverse Client Sampling for Fasting Federated Learning. [[PUB](https://openreview.net/forum?id=6XC5iKqRVm)] [[SUPP](https://openreview.net/attachment?id=6XC5iKqRVm&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2205.13925)] - Federated Compositional Deep AUC Maximization. [[PUB](https://openreview.net/forum?id=tF7W8ai8J3)] [[SUPP](https://openreview.net/attachment?id=tF7W8ai8J3&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2304.10101)] - A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning. [[PUB](https://openreview.net/forum?id=S6ajVZy6FA)] [[SUPP](https://openreview.net/attachment?id=S6ajVZy6FA&name=supplementary_material)] [[CODE](https://github.com/hfzhang31/A3FL)] - Flow: Per-instance Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=BI031mw7iS)] [[SUPP](https://openreview.net/attachment?id=BI031mw7iS&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2211.15281)] [[CODE](https://github.com/Astuary/Flow)] - Eliminating Domain Bias for Federated Learning in Representation Space. [[PUB](https://openreview.net/forum?id=nO5i1XdUS0)] [[SUPP](https://openreview.net/attachment?id=nO5i1XdUS0&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.14975)] [[CODE](https://github.com/TsingZ0/DBE)] - Federated Learning with Manifold Regularization and Normalized Update Reaggregation. [[PUB](https://openreview.net/forum?id=7uPnuoYqac)] [[SUPP](https://openreview.net/attachment?id=7uPnuoYqac&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.05924)] - Structured Federated Learning through Clustered Additive Modeling. [[PUB](https://openreview.net/forum?id=2XT3UpOv48)] [[SUPP](https://openreview.net/attachment?id=2XT3UpOv48&name=supplementary_material)] - Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer. [[PUB](https://openreview.net/forum?id=gJewjFjfN2)] [[SUPP](https://openreview.net/attachment?id=gJewjFjfN2&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.07587)] [[CODE](https://github.com/ZackZikaiXiao/FedGraB)] - Dynamic Personalized Federated Learning with Adaptive Differential Privacy. [[PUB](https://openreview.net/forum?id=RteNLuc8D9)] [[SUPP](https://openreview.net/attachment?id=RteNLuc8D9&name=supplementary_material)] [[CODE](https://github.com/xiyuanyang45/DynamicPFL)] - Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. [[PUB](https://openreview.net/forum?id=dEDdRWunxU)] [[SUPP](https://openreview.net/attachment?id=dEDdRWunxU&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2312.13923)] [[CODE](https://github.com/zhyczy/Fed-CO2)] - Solving a Class of Non-Convex Minimax Optimization in Federated Learning. [[PUB](https://openreview.net/forum?id=SpStmVboGy)] [[SUPP](https://openreview.net/attachment?id=SpStmVboGy&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.03613)] [[CODE](https://github.com/xidongwu/Federated-Minimax-and-Conditional-Stochastic-Optimization/)] - Federated Learning via Meta-Variational Dropout. [[PUB](https://openreview.net/forum?id=VNyKBipt91)] [[CODE](https://github.com/insujeon/MetaVD)] - Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates. [[PUB](https://openreview.net/forum?id=0ORqsMY6OL)] [[SUPP](https://openreview.net/attachment?id=0ORqsMY6OL&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.19807)] - SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning. [[PUB](https://openreview.net/forum?id=tmxjuIFSEc)] [[CODE](https://github.com/culiver/SPACE)] - Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense. [[PUB](https://openreview.net/forum?id=txPdKZrrZF)] [[SUPP](https://openreview.net/attachment?id=txPdKZrrZF&name=supplementary_material)] - FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. [[PUB](https://openreview.net/forum?id=phnGilhPH8)] [[PDF](https://arxiv.org/abs/2310.05077)] [[CODE](https://github.com/visitworld123/fedfed)] - PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. [[PUB](https://openreview.net/forum?id=kuxu4lCRr5)] [[SUPP](https://openreview.net/attachment?id=kuxu4lCRr5&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.09183)] [[CODE](https://github.com/bdemo/pfedbred_public)] [[解读](https://zhuanlan.zhihu.com/p/661506638)] - Spectral Co-Distillation for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=RqjQL08UFc)] - Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy. [[PUB](https://openreview.net/forum?id=4ZaPpVDjGQ)] [[SUPP](https://openreview.net/attachment?id=4ZaPpVDjGQ&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.09624)] - Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation. [[PUB](https://openreview.net/forum?id=7ETbK9lQd7)] [[SUPP](https://openreview.net/attachment?id=7ETbK9lQd7&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.04924)] [[CODE](https://github.com/BerivanIsik/rrsc)] - (Amplified) Banded Matrix Factorization: A unified approach to private training. [[PUB](https://openreview.net/forum?id=zEm6hF97Pz)] [[SUPP](https://openreview.net/attachment?id=zEm6hF97Pz&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.08153)] - Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices. [[PUB](https://openreview.net/forum?id=nXNsqB4Yr1)] [[SUPP](https://openreview.net/attachment?id=nXNsqB4Yr1&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2305.17005)] [[CODE](https://github.com/k1l1/SLT)] - Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. [[PUB](https://openreview.net/forum?id=izNfcaHJk0)] [[SUPP](https://openreview.net/attachment?id=izNfcaHJk0&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2304.01541)] - Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. [[PUB](https://openreview.net/forum?id=g2ROKOASiv)] [[SUPP](https://openreview.net/attachment?id=g2ROKOASiv&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2305.16272)] - Resilient Constrained Learning. [[PUB](https://openreview.net/forum?id=h0RVoZuUl6)] [[SUPP](https://openreview.net/attachment?id=h0RVoZuUl6&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.02426)] - A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. [[PUB](https://openreview.net/forum?id=loxinzXlCx)] [[SUPP](https://openreview.net/attachment?id=loxinzXlCx&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2205.15580)] [[CODE](https://github.com/mysteryresearcher/dasha-partial-participation)] - Collaboratively Learning Linear Models with Structured Missing Data. [[PUB](https://openreview.net/forum?id=waDF0oACu2)] [[SUPP](https://openreview.net/attachment?id=waDF0oACu2&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2307.11947)] [[CODE](https://github.com/garyxcheng/collab)] - Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. [[PUB](https://openreview.net/forum?id=qCglMj6A4z)] [[SUPP](https://openreview.net/attachment?id=qCglMj6A4z&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.01463)] - Fast Optimal Locally Private Mean Estimation via Random Projections. [[PUB](https://openreview.net/forum?id=K3JgUvDSYX)] [[SUPP](https://openreview.net/attachment?id=K3JgUvDSYX&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.04444)] [[CODE](https://github.com/apple/ml-projunit)] - Contextual Stochastic Bilevel Optimization. [[PUB](https://openreview.net/forum?id=SHBksHKutP)] [[SUPP](https://openreview.net/attachment?id=SHBksHKutP&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.18535)] - Understanding Deep Gradient Leakage via Inversion Influence Functions. [[PUB](https://openreview.net/forum?id=tBib2fWr3r)] [[SUPP](https://openreview.net/attachment?id=tBib2fWr3r&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.13016)] [[CODE](https://github.com/illidanlab/inversion-influence-function)] - Inner Product-based Neural Network Similarity. [[PUB](https://openreview.net/forum?id=9eneYFIGKq)] [[SUPP](https://openreview.net/attachment?id=9eneYFIGKq&name=supplementary_material)] - Correlation Aware Sparsified Mean Estimation Using Random Projection. [[PUB](https://openreview.net/forum?id=VacSQpbI0U)] [[SUPP](https://openreview.net/attachment?id=VacSQpbI0U&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.18868)] [[CODE](https://github.com/11hifish/Rand-Proj-Spatial)] - TIES-Merging: Resolving Interference When Merging Models. [[PUB](https://openreview.net/forum?id=xtaX3WyCj1)] [[SUPP](https://openreview.net/attachment?id=xtaX3WyCj1&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.01708)] [[CODE](https://github.com/prateeky2806/ties-merging)] - Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data. [[PUB](https://openreview.net/forum?id=qyixBZl8Ph)] [[SUPP](https://openreview.net/attachment?id=qyixBZl8Ph&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2305.04792)] [[CODE](https://github.com/aparna-aketi/global_update_tracking)] - Large-Scale Distributed Learning via Private On-Device LSH. [[PUB](https://openreview.net/forum?id=dpdbbN7AKr)] [[SUPP](https://openreview.net/attachment?id=dpdbbN7AKr&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.02563)] - Faster Relative Entropy Coding with Greedy Rejection Coding. [[PUB](https://openreview.net/forum?id=KXbAgvLi2l)] [[SUPP](https://openreview.net/attachment?id=KXbAgvLi2l&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.15746)] [[CODE](https://github.com/cambridge-mlg/fast-rec-with-grc)] - Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization. [[PUB](https://openreview.net/forum?id=gVLKXT9JwG)] [[SUPP](https://openreview.net/attachment?id=gVLKXT9JwG&name=supplementary_material)] - Momentum Provably Improves Error Feedback!. [[PUB](https://openreview.net/forum?id=1h92PmnKov)] [[SUPP](https://openreview.net/attachment?id=1h92PmnKov&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2305.15155)] - Strategic Data Sharing between Competitors. [[PUB](https://openreview.net/forum?id=AkK3S2spZs)] [[SUPP](https://openreview.net/attachment?id=AkK3S2spZs&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2305.16052)] - H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets. [[PUB](https://openreview.net/forum?id=M4h1UAxI3b)] - Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/431d53d513461ff155d5bc8faa9a440c-Abstract-Conference.html)] - Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/662bb9c4dcc96aeaac8e7cd3fc6a0add-Abstract-Datasets_and_Benchmarks.html)] [[CODE](https://github.com/google-research/dataset_grouper)] - Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/02b9d1e6d1b5295a6f883969ddc1bbbd-Abstract-Datasets_and_Benchmarks.html)] - Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/7c72fcd7b6bffc3864c7152ab5a2dd83-Abstract-Conference.html)] - Collaborative Learning via Prediction Consensus. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html)] - Incentives in Private Collaborative Machine Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/180f1a1de4244c009ff0848c55ae54a5-Abstract-Conference.html)] - Robust and Actively Secure Serverless Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/7c5a4b7a31dffef8ce296deedb6214a9-Abstract-Conference.html)] - Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/5b4a459db23e6db9be2a128380953d96-Abstract-Conference.html)] - Swarm Reinforcement Learning for Adaptive Mesh Refinement. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/e85454a113e8b41e017c81875ae68d47-Abstract-Conference.html)] #### NeurIPS Datasets and Benchmarks - Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [[PUB](https://openreview.net/forum?id=qynH28Y4xE)] [[SUPP](https://openreview.net/attachment?id=qynH28Y4xE&name=supplementary_material)] [[DATASET](https://huggingface.co/datasets/wyzelabs/RuleRecommendation)] - Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [[PUB](https://openreview.net/forum?id=EPz1DcdPVE)] [[PDF](https://arxiv.org/abs/2307.09619)] [[DATASET](https://github.com/google-research/dataset_grouper)] [[CODE](https://github.com/google-research/dataset_grouper)] #### NeurIPS workshop - Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [[PUB](https://openreview.net/forum?id=8zduZGpzZl)] - HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning. [[PUB](https://openreview.net/forum?id=dsWg7n6zoo)] - Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning. [[PUB](https://openreview.net/forum?id=H0inHCV05c)] - FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data. [[PUB](https://openreview.net/forum?id=XJhL1XlefX)] - FedSoL: Bridging Global Alignment and Local Generality in Federated Learning. [[PUB](https://openreview.net/forum?id=WYLhRgBAFH)] - One-shot Empirical Privacy Estimation for Federated Learning. [[PUB](https://openreview.net/forum?id=JmrHzzDiyI)] - Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning. [[PUB](https://openreview.net/forum?id=5JsO2DClwk)] - SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models. [[PUB](https://openreview.net/forum?id=06quMTmtRV)] - The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. [[PUB](https://openreview.net/forum?id=xqvB784PCv)] - Towards Building the FederatedGPT: Federated Instruction Tuning. [[PUB](https://openreview.net/forum?id=TaDiklyVps)] - Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR. [[PUB](https://openreview.net/forum?id=ozN92d7CHX)] - LASER: Linear Compression in Wireless Distributed Optimization. [[PUB](https://openreview.net/forum?id=PmahoyE89G)] - MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization. [[PUB](https://openreview.net/forum?id=YqqWQP8POe)] - TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation. [[PUB](https://openreview.net/forum?id=SvJx4a75QZ)] - An Empirical Evaluation of Federated Contextual Bandit Algorithms. [[PUB](https://openreview.net/forum?id=qwnOt7FFSD)] - RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. [[PUB](https://openreview.net/forum?id=FakNykU4PF)] - FDAPT: Federated Domain-adaptive Pre-training for Language Models. [[PUB](https://openreview.net/forum?id=ESCL5T3EgV)] - Making Batch Normalization Great in Federated Deep Learning. [[PUB](https://openreview.net/forum?id=iKQC652XIk)] - Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. [[PUB](https://openreview.net/forum?id=AbrnDOw8R9)] - Parameter Averaging Laws for Multitask Language Models. [[PUB](https://openreview.net/forum?id=qQ2qXFu05s)] - Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [[PUB](https://openreview.net/forum?id=HyRwexERAo)] - Beyond Parameter Averaging in Model Aggregation. [[PUB](https://openreview.net/forum?id=sPtEDSVD4K)] - Augmenting Federated Learning with Pretrained Transformers. [[PUB](https://openreview.net/forum?id=ldN6QdyukS)] - Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization. [[PUB](https://openreview.net/forum?id=le0Emy9SqA)] - DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. [[PUB](https://openreview.net/forum?id=s7hquGszME)] - Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning. [[PUB](https://openreview.net/forum?id=gACRiXPGmM)] - FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System. [[PUB](https://openreview.net/forum?id=PuYD0fh5aq)] - Learning Optimizers for Local SGD. [[PUB](https://openreview.net/forum?id=HiPe4SjZMs)] - Exploring User-level Gradient Inversion with a Diffusion Prior. [[PUB](https://openreview.net/forum?id=lcElZPvMFp)] - User Inference Attacks on Large Language Models. [[PUB](https://openreview.net/forum?id=4uyyLG4KCH)] - FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis. [[PUB](https://openreview.net/forum?id=1ww9tjEQVL)] - Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models. [[PUB](https://openreview.net/forum?id=EmV9sGpZ7q)] - Backdoor Threats from Compromised Foundation Models to Federated Learning. [[PUB](https://openreview.net/forum?id=BrcHuO2BVc)] - MOFL/D: A Federated Multi-objective Learning Framework with Decomposition. [[PUB](https://openreview.net/forum?id=Pj6BPHZy56)] - Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning. [[PUB](https://openreview.net/forum?id=OoEIUohfcp)] - Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models. [[PUB](https://openreview.net/forum?id=LiSj1GRVhL)] - FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning. [[PUB](https://openreview.net/forum?id=4apX9Kcxie)] - Private and Personalized Histogram Estimation in a Federated Setting. [[PUB](https://openreview.net/forum?id=XSfsvBoc8M)] #### COLT - The Aggregation–Heterogeneity Trade-off in Federated Learning. [[PUB](https://proceedings.mlr.press/v195/zhao23b.html)] #### UAI - FLASH: Automating federated learning using CASH. [[PUB](https://openreview.net/forum?id=5L66DZpPSHk)] [[SUPP](https://proceedings.mlr.press/v216/alam23a/alam23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=5L66DZpPSHk&name=other_supplementary_material)] - Personalized federated domain adaptation for item-to-item recommendation. [[PUB](https://openreview.net/forum?id=7ypu4_en3Zm)] [[PDF](https://arxiv.org/abs/2306.03191)] [[SUPP](https://proceedings.mlr.press/v216/fan23a/fan23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=7ypu4_en3Zm&name=other_supplementary_material)] [[CODE](https://github.com/zfan20/PFGNNPlus)] - Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. [[PUB](https://openreview.net/forum?id=Q06wKxnHRv)] [[PDF](https://arxiv.org/abs/2110.00532)] [[SUPP](https://proceedings.mlr.press/v216/karimi23a/karimi23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=Q06wKxnHRv&name=other_supplementary_material)] - Federated learning of models pre-trained on different features with consensus graphs. [[PUB](https://openreview.net/forum?id=gSMiXJmMEOf)] [[SUPP](https://proceedings.mlr.press/v216/ma23b/ma23b-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=gSMiXJmMEOf&name=other_supplementary_material)] [[CODE](https://github.com/matenure/federated_feature_fusion)] - Fast Heterogeneous Federated Learning with Hybrid Client Selection. [[PUB](https://openreview.net/forum?id=JtSlA972EHP)] [[SUPP](https://proceedings.mlr.press/v216/song23b/song23b-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=JtSlA972EHP&name=other_supplementary_material)] [[PDF](https://arxiv.org/abs/2208.05135)] - Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. [[PUB](https://openreview.net/forum?id=Gt_GiNkBhu)] [[PDF](https://arxiv.org/abs/2210.10880)] [[SUPP](https://proceedings.mlr.press/v216/wu23a/wu23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=Gt_GiNkBhu&name=other_supplementary_material)] [[CODE](https://github.com/wrh14/learning_to_invert)] #### ICML - Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. [[PUB](https://openreview.net/forum?id=vD1R00hROK)] [[PDF](https://arxiv.org/abs/2305.11584)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/24651.pdf)] - Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. [[PUB](https://openreview.net/forum?id=wbs1fKLfOe)] [[PDF](https://arxiv.org/abs/2211.14292)] - FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. [[PUB](https://openreview.net/forum?id=891ytYlYgB)] [[PDF](https://arxiv.org/abs/2206.03966)] [[CODE](https://github.com/alibaba/FederatedScope/tree/master/benchmark/FedHPOBench)] - Federated Conformal Predictors for Distributed Uncertainty Quantification. [[PUB](https://openreview.net/forum?id=YVTr9PzIrK)] [[PDF](https://arxiv.org/abs/2305.17564)] [[CODE](https://github.com/clu5/federated-conformal)] - Federated Adversarial Learning: A Framework with Convergence Analysis. [[PUB](https://openreview.net/forum?id=kgvoV2KcTJ)] [[PDF](https://arxiv.org/abs/2208.03635)] - Federated Heavy Hitter Recovery under Linear Sketching. [[PUB](https://openreview.net/forum?id=zN4oRCrlnM)] [[PDF](https://arxiv.org/abs/2307.13347)] [[CODE](https://github.com/google-research/federated)] - Doubly Adversarial Federated Bandits. [[PUB](https://openreview.net/forum?id=FjOB0g7iRf)] [[PDF](https://arxiv.org/abs/2301.09223)] [[CODE](https://github.com/jialinyi94/doubly-stochastic-federataed-bandit)] - Achieving Linear Speedup in Non-IID Federated Bilevel Learning. [[PUB](https://openreview.net/forum?id=XFpTtAWNpQ)] [[PDF](https://arxiv.org/abs/2302.05412)] - One-Shot Federated Conformal Prediction. [[PUB](https://openreview.net/forum?id=SZJGIWe1Ag)] [[PDF](https://arxiv.org/abs/2302.06322)] [[CODE](https://github.com/pierreHmbt/FedCP-QQ)] - Federated Online and Bandit Convex Optimization. [[PUB](https://openreview.net/forum?id=mi7pnouqLa)] - Federated Linear Contextual Bandits with User-level Differential Privacy. [[PUB](https://openreview.net/forum?id=b9opfVNw6O)] [[PDF](https://arxiv.org/abs/2306.05275)] - Vertical Federated Graph Neural Network for Recommender System. [[PUB](https://openreview.net/forum?id=NRnS6CtbaN)] [[PDF](https://arxiv.org/abs/2303.05786)] [[CODE](https://github.com/maiph123/verticalgnn)] - Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. [[PUB](https://openreview.net/forum?id=IYyhNudD9V)] [[PDF](https://arxiv.org/abs/2302.04969)] - Towards Understanding Ensemble Distillation in Federated Learning. [[PUB](https://openreview.net/forum?id=Xx0TH4IKgQ)] - Personalized Subgraph Federated Learning. [[PUB](https://openreview.net/forum?id=GXHL8ZS1GX)] [[PDF](https://arxiv.org/abs/2206.10206)] [[CODE](https://github.com/Kang-Min-Ku/CUFL.git)] - Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. [[PUB](https://openreview.net/forum?id=ytpEqHYSEy)] [[PDF](https://arxiv.org/abs/2306.05131)] - Secure Federated Correlation Test and Entropy Estimation. [[PUB](https://openreview.net/forum?id=ICk7GJ1awE)] [[PDF](https://arxiv.org/abs/2105.14618)] - Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. [[PUB](https://openreview.net/forum?id=JC05k0E2EM)] [[CODE](https://github.com/YamingGuo98/FedIIR)] - Personalized Federated Learning under Mixture of Distributions. [[PUB](https://openreview.net/forum?id=nmVOTsQGR9)] [[PDF](https://arxiv.org/abs/2305.01068)] [[CODE](https://github.com/zshuai8/FedGMM_ICML2023)] - FedDisco: Federated Learning with Discrepancy-Aware Collaboration. [[PUB](https://openreview.net/forum?id=cHJ1VuZorx)] [[PDF](https://arxiv.org/abs/2305.19229)] [[CODE](https://github.com/MediaBrain-SJTU/FedDisco)] - Anchor Sampling for Federated Learning with Partial Client Participation. [[PUB](https://openreview.net/forum?id=Ht9r3P6Lts)] [[PDF](https://arxiv.org/abs/2206.05891)] [[CODE](https://github.com/harliwu/fedamd)] - Private Federated Learning with Autotuned Compression. [[PUB](https://openreview.net/forum?id=y8qAZhWbNs)] [[PDF](https://arxiv.org/abs/2307.10999)] - Fast Federated Machine Unlearning with Nonlinear Functional Theory. [[PUB](https://openreview.net/forum?id=6wQKmKiDHw)] - On the Convergence of Federated Averaging with Cyclic Client Participation. [[PUB](https://openreview.net/forum?id=d8LTNXt97w)] [[PDF](https://arxiv.org/abs/2302.03109)] - Revisiting Weighted Aggregation in Federated Learning with Neural Networks. [[PUB](https://openreview.net/forum?id=FuDAjnWhrQ)] [[PDF](https://arxiv.org/abs/2302.10911)] [[CODE](https://github.com/zexilee/icml-2023-fedlaw)] - The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond. [[PUB](https://openreview.net/forum?id=WfI3I8OjHS)] [[PDF](https://arxiv.org/abs/2305.10697)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/24679_ljO6pDE.pdf)] - GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=iASUTBGw07)] - Flash: Concept Drift Adaptation in Federated Learning. [[PUB](https://openreview.net/forum?id=q5RHsg6VRw)] - DoCoFL: Downlink Compression for Cross-Device Federated Learning. [[PUB](https://openreview.net/forum?id=VxKr51JjWC)] [[PDF](https://arxiv.org/abs/2302.00543)] - FeDXL: Provable Federated Learning for Deep X-Risk Optimization. [[PUB](https://openreview.net/forum?id=C7fNCYdptO)] [[PDF](https://arxiv.org/abs/2210.14396)] [[CODE](https://github.com/optimization-ai/icml2023_fedxl)] - No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation. [[PUB](https://openreview.net/forum?id=AMuNQEUmGr)] [[CODE](https://github.com/Hypervoyager/PFL)] - Personalized Federated Learning with Inferred Collaboration Graphs. [[PUB](https://openreview.net/forum?id=33fj5Ph3ot)] [[CODE](https://github.com/MediaBrain-SJTU/pFedGraph)] - Optimizing the Collaboration Structure in Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=rnNBSMOWvA)] [[PDF](https://arxiv.org/abs/2306.06508)] [[CODE](https://github.com/baowenxuan/fedcollab)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/23569.pdf)] - TabLeak: Tabular Data Leakage in Federated Learning. [[PUB](https://openreview.net/forum?id=mRiDy4qGwB)] [[PDF](https://arxiv.org/abs/2210.01785)] [[CODE](https://github.com/eth-sri/tableak)] - FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization. [[PUB](https://openreview.net/forum?id=YDC5jTS3LR)] [[CODE](https://github.com/haozzh/FedCR)] - Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. [[PUB](https://openreview.net/forum?id=NcbY2UOfko)] [[PDF](https://arxiv.org/abs/2209.15245)] - Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. [[PUB](https://openreview.net/forum?id=Otdp5SGQMr)] [[PDF](https://arxiv.org/abs/2211.03942)] [[CODE](https://github.com/facebookresearch/dp_compression)] - SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. [[PUB](https://openreview.net/forum?id=pRsJIVcjxD)] [[PDF](https://arxiv.org/abs/2306.07644)] [[CODE](https://github.com/owkin/sratta)] - Improving the Model Consistency of Decentralized Federated Learning. [[PUB](https://openreview.net/forum?id=fn2NFlYLBL)] [[PDF](https://arxiv.org/abs/2302.04083)] - Efficient Personalized Federated Learning via Sparse Model-Adaptation. [[PUB](https://openreview.net/forum?id=ieSN7Xyo8g)] [[PDF](https://arxiv.org/abs/2305.02776)] [[CODE](https://github.com/yxdyc/pfedgate)] - From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. [[PUB](https://openreview.net/forum?id=CBLDv6SFMn)] [[PDF](https://arxiv.org/abs/2302.12559)] [[CODE](https://github.com/totilas/padadmm)] - LeadFL: Client Self-Defense against Model Poisoning in Federated Learning. [[PUB](https://openreview.net/forum?id=2CiaH2Tq4G)] [[CODE](https://github.com/chaoyitud/LeadFL)] - Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. [[PUB](https://openreview.net/forum?id=HtHFnHrZXu)] [[PDF](https://arxiv.org/abs/2304.12961)] [[CODE](https://github.com/ybdai7/chameleon-durable-backdoor)] - FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. [[PUB](https://openreview.net/forum?id=7aqVcrXjxa)] [[PDF](https://arxiv.org/abs/2304.13407)] - FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. [[PUB](https://openreview.net/forum?id=nDKoVwNjMH)] [[PDF](https://arxiv.org/abs/2205.13462)] [[CODE](https://github.com/lins-lab/fedbr)] - Towards Unbiased Training in Federated Open-world Semi-supervised Learning. [[PUB](https://openreview.net/forum?id=gHfybro5Sj)] [[PDF](https://arxiv.org/abs/2305.00771)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/25109.pdf)] - Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. [[PUB](https://openreview.net/forum?id=Ai1TyAjZt9)] [[PDF](https://arxiv.org/abs/2209.05578)] - Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. [[PUB](https://openreview.net/forum?id=Kz0IODB2kj)] [[PDF](https://arxiv.org/abs/2306.00127)] [[CODE](https://github.com/junyizhu-ai/surrogate_model_extension)] - Fair yet Asymptotically Equal Collaborative Learning. [[PUB](https://openreview.net/forum?id=5VhltFPSO8)] [[PDF](https://arxiv.org/abs/2306.05764)] [[CODE](https://github.com/xqlin98/Fair-yet-Equal-CML)] - Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. [[PUB](https://openreview.net/forum?id=uIzkbJgyqc)] [[PDF](https://arxiv.org/abs/2210.08371)] - Adversarial Collaborative Learning on Non-IID Features. [[PUB](https://openreview.net/forum?id=DVF7gEQQf7)] - XTab: Cross-table Pretraining for Tabular Transformers. [[PUB](https://openreview.net/forum?id=uGORNDmIdr)] [[PDF](https://arxiv.org/abs/2305.06090)] [[CODE](https://github.com/bingzhaozhu/xtab)] - Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. [[PUB](https://openreview.net/forum?id=a0kGwNUwil)] - Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting. [[PUB](https://openreview.net/forum?id=3DI6Kmw81p)] [[PDF](https://arxiv.org/abs/2302.06079)] [[CODE](https://github.com/YuchenLiu-a/byzantine-gas)] - LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=L8iWCxzwl1)] [[PDF](https://arxiv.org/abs/2305.02219)] - FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks. [[PUB](https://openreview.net/forum?id=eqTWOzheZT)] - Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. [[PUB](https://openreview.net/forum?id=iAgQfF3atY)] [[PDF](https://arxiv.org/abs/2306.02543)] [[CODE](https://github.com/boxinz17/data-market-via-adaptive-sampling)] - Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. [[PUB](https://proceedings.mlr.press/v202/kandpal23b.html)] - RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. [[PUB](https://proceedings.mlr.press/v202/li23i.html)] - Robust Collaborative Learning with Linear Gradient Overhead. [[PUB](https://proceedings.mlr.press/v202/farhadkhani23a.html)] [[CODE](https://github.com/LPD-EPFL/robust-collaborative-learning)] #### Mach Learn - Ensemble and continual federated learning for classification tasks. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06330-z)] [[PDF](https://arxiv.org/abs/2006.07129)] - FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06360-7)] - Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [[PUB](https://link.springer.com/article/10.1007/s10994-022-06292-8)] #### JMLR - FedLab: A Flexible Federated Learning Framework :fire:. [[PUB](https://jmlr.org/papers/v24/22-0440.html)] [[PDF](https://arxiv.org/abs/2107.11621)] [[CODE](https://github.com/SMILELab-FL/FedLab)] - Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?. [[PUB](https://jmlr.org/papers/v24/21-0224.html)] - Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. [[PUB](https://jmlr.org/papers/v24/21-1301.html)] [[PDF](https://arxiv.org/abs/2106.04911)] [[CODE](https://github.com/bokun-wang/moml)] - A First Look into the Carbon Footprint of Federated Learning. [[PUB](https://jmlr.org/papers/v24/21-0445.html)] [[PDF](https://arxiv.org/abs/2102.07627)] - Attacks against Federated Learning Defense Systems and their Mitigation. [[PUB](https://jmlr.org/papers/v24/22-0014.html)] [[CODE](https://github.com/codymlewis/viceroy)] - A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. [[PUB](https://jmlr.org/papers/v24/22-0689.html)] [[PDF](https://arxiv.org/abs/2206.10189)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous_FL)] - FedLab: A Flexible Federated Learning Framework. [[PUB](http://jmlr.org/papers/v24/22-0440.html)] - A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points. [[PUB](https://jmlr.org/papers/v24/22-0153.html)] - Multi-view Collaborative Gaussian Process Dynamical Systems. [[PUB](http://jmlr.org/papers/v24/19-094.html)] - Variational Inference for Deblending Crowded Starfields. [[PUB](https://jmlr.org/papers/v24/21-0169.html)] #### TPAMI - Tighter Regret Analysis and Optimization of Online Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10255290)] [[PDF](https://arxiv.org/abs/2205.06491)] - Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup. [[PDF](https://arxiv.org/abs/2308.00522)] - Federated Learning Via Inexact ADMM. [[PUB](https://ieeexplore.ieee.org/document/10040221)] [[PDF](https://arxiv.org/abs/2204.10607)] [[CODE](https://github.com/ShenglongZhou/FedADMM)] - FedIPR: Ownership Verification for Federated Deep Neural Network Models. [[PUB](https://ieeexplore.ieee.org/document/9847383)] [[PDF](https://arxiv.org/abs/2109.13236)] [[CODE](https://github.com/purp1eHaze/FedIPR)] [[解读](https://zhuanlan.zhihu.com/p/562837170)] - Decentralized Federated Averaging. [[PUB](https://ieeexplore.ieee.org/document/9850408)] [[PDF](https://arxiv.org/abs/2104.11375)] - Attribute-Guided Collaborative Learning for Partial Person Re-Identification. [[PUB](https://doi.org/10.1109/TPAMI.2023.3312302)] - Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling. [[PUB](https://doi.org/10.1109/TPAMI.2022.3141095)] #### ICLR - Personalized Federated Learning with Feature Alignment and Classifier Collaboration. [[PUB](https://openreview.net/forum?id=SXZr8aDKia)] [[CODE](https://github.com/JianXu95/FedPAC)] - MocoSFL: enabling cross-client collaborative self-supervised learning. [[PUB](https://openreview.net/forum?id=2QGJXyMNoPz)] [[CODE](https://github.com/SonyAI/MocoSFL)] - Single-shot General Hyper-parameter Optimization for Federated Learning. [[PUB](https://openreview.net/forum?id=3RhuF8foyPW)] [[PDF](https://arxiv.org/abs/2202.08338)] [[CODE](https://openreview.net/attachment?id=3RhuF8foyPW&name=SUPP_material)] - Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated. [[PUB](https://openreview.net/forum?id=Mpa3tRJFBb)] [[PDF](https://arxiv.org/abs/2206.15387)] [[CODE](https://github.com/facebookresearch/where_to_begin)] - FedExP: Speeding up Federated Averaging via Extrapolation. [[PUB](https://openreview.net/forum?id=IPrzNbddXV)] [[PDF](https://arxiv.org/abs/2301.09604)] [[CODE](https://github.com/divyansh03/fedexp)] - Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection. [[PUB](https://openreview.net/forum?id=mMNimwRb7Gr)] [[CODE](https://github.com/illidanlab/FOSTER)] - DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. [[PUB](https://openreview.net/forum?id=VA1YpcNr7ul)] [[PDF](https://arxiv.org/abs/2202.01268)] [[CODE](https://github.com/mysteryresearcher/dasha)] - Machine Unlearning of Federated Clusters. [[PUB](https://openreview.net/forum?id=VzwfoFyYDga)] [[PDF](https://arxiv.org/abs/2210.16424)] [[CODE](https://openreview.net/attachment?id=VzwfoFyYDga&name=SUPP_material)] - Federated Neural Bandits. [[PUB](https://openreview.net/forum?id=38m4h8HcNRL)] [[PDF](https://arxiv.org/abs/2205.14309)] [[CODE](https://openreview.net/attachment?id=38m4h8HcNRL&name=SUPP_material)] - FedFA: Federated Feature Augmentation. [[PUB](https://openreview.net/forum?id=U9yFP90jU0)] [[PDF](https://arxiv.org/abs/2301.12995)] [[CODE](https://github.com/tfzhou/fedfa)] - Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. [[PUB](https://openreview.net/forum?id=dZrQR7OR11)] [[PDF](https://arxiv.org/abs/2302.04228)] [[CODE](https://github.com/hanguo97/expectation-propagation)] - Better Generative Replay for Continual Federated Learning. [[PUB](https://openreview.net/forum?id=cRxYWKiTan)] [[CODE](https://github.com/daiqing98/FedCIL)] - Federated Learning from Small Datasets. [[PUB](https://openreview.net/forum?id=hDDV1lsRV8)] [[PDF](https://arxiv.org/abs/2110.03469)] - Federated Nearest Neighbor Machine Translation. [[PUB](https://openreview.net/forum?id=R1U5G2spbLd)] [[PDF](https://arxiv.org/abs/2302.12211)] - Meta Knowledge Condensation for Federated Learning. [[PUB](https://openreview.net/forum?id=TDf-XFAwc79)] [[PDF](https://arxiv.org/abs/2209.14851)] - Test-Time Robust Personalization for Federated Learning. [[PUB](https://openreview.net/forum?id=3aBuJEza5sq)] [[PDF](https://arxiv.org/abs/2205.10920)] [[CODE](https://openreview.net/attachment?id=3aBuJEza5sq&name=SUPP_material)] - DepthFL : Depthwise Federated Learning for Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=pf8RIZTMU58)] - Towards Addressing Label Skews in One-Shot Federated Learning. [[PUB](https://openreview.net/forum?id=rzrqh85f4Sc)] [[CODE](https://openreview.net/attachment?id=rzrqh85f4Sc&name=SUPP_material)] - Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=EXnIyMVTL8s)] [[PDF](https://arxiv.org/abs/2210.00226)] [[CODE](https://github.com/Yujun-Shi/FedCLS)] - Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. [[PUB](https://openreview.net/forum?id=A9WQaxYsfx)] [[CODE](https://openreview.net/attachment?id=A9WQaxYsfx&name=SUPP_material)] - SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. [[PUB](https://openreview.net/forum?id=jh1nCir1R3d)] [[PDF](https://arxiv.org/abs/2210.14026)] [[CODE](https://openreview.net/attachment?id=jh1nCir1R3d&name=SUPP_material)] - Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. [[PUB](https://openreview.net/forum?id=TVY6GoURrw)] [[PDF](https://arxiv.org/abs/2106.09779)] [[CODE](https://github.com/lowya/private-federated-learning-without-a-trusted-server)] - Effective passive membership inference attacks in federated learning against overparameterized models. [[PUB](https://openreview.net/forum?id=QsCSLPP55Ku)] - FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. [[PUB](https://openreview.net/forum?id=9aokcgBVIj1)] [[PDF](https://arxiv.org/abs/2206.08671)] [[CODE](https://openreview.net/attachment?id=9aokcgBVIj1&name=SUPP_material)] - Multimodal Federated Learning via Contrastive Representation Ensemble. [[PUB](https://openreview.net/forum?id=Hnk1WRMAYqg)] [[PDF](https://arxiv.org/abs/2302.08888)] [[CODE](https://github.com/flair-thu/creamfl)] - Faster federated optimization under second-order similarity. [[PUB](https://openreview.net/forum?id=ElC6LYO4MfD)] [[PDF](https://arxiv.org/abs/2209.02257)] [[CODE](https://openreview.net/attachment?id=ElC6LYO4MfD&name=SUPP_material)] - FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. [[PUB](https://openreview.net/forum?id=bZjxxYURKT)] [[CODE](https://openreview.net/attachment?id=bZjxxYURKT&name=SUPP_material)] - The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. [[PUB](https://openreview.net/forum?id=29V3AWjVAFi)] [[PDF](https://arxiv.org/abs/2301.08968)] [[CODE](https://openreview.net/attachment?id=29V3AWjVAFi&name=SUPP_material)] - PerFedMask: Personalized Federated Learning with Optimized Masking Vectors. [[PUB](https://openreview.net/forum?id=hxEIgUXLFF)] [[CODE](https://github.com/MehdiSet/PerFedMask)] - EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. [[PUB](https://openreview.net/forum?id=ytZIYmztET)] [[CODE](https://github.com/MingruiLiu-ML-Lab/episode)] - FedDAR: Federated Domain-Aware Representation Learning. [[PUB](https://openreview.net/forum?id=6P9Y25Pljl6)] [[PDF](https://arxiv.org/abs/2209.04007)] [[CODE](https://github.com/zlz0414/FedDAR)] - Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. [[PUB](https://openreview.net/forum?id=oJpVVGXu9i)] [[CODE](https://github.com/shenzebang/CENTAUR-Privacy-Federated-Representation-Learning)] - FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. [[PUB](https://openreview.net/forum?id=Xo2E217_M4n)] [[PDF](https://arxiv.org/abs/2210.12873)] [[CODE](https://github.com/KaiyuanZh/FLIP)] - Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. [[PUB](https://openreview.net/forum?id=-EHqoysUYLx)] - Efficient Federated Domain Translation. [[PUB](https://openreview.net/forum?id=uhLAcrAZ9cJ)] [[CODE](https://openreview.net/attachment?id=uhLAcrAZ9cJ&name=SUPP_material)] - On the Importance and Applicability of Pre-Training for Federated Learning. [[PUB](https://openreview.net/forum?id=fWWFv--P0xP)] [[PDF](https://arxiv.org/abs/2206.11488)] [[CODE](https://github.com/andytu28/fps_pre-training)] - Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. [[PUB](https://openreview.net/forum?id=r0BrY4BiEXO)] [[PDF](https://arxiv.org/abs/2201.12675)] [[CODE](https://github.com/JonasGeiping/breaching)] - A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. [[PUB](https://openreview.net/forum?id=FUiDMCr_W4o)] [[PDF](https://arxiv.org/abs/2207.01771)] - Instance-wise Batch Label Restoration via Gradients in Federated Learning. [[PUB](https://openreview.net/forum?id=FIrQfNSOoTr)] [[CODE](https://github.com/BUAA-CST/iLRG)] - Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. [[PUB](https://openreview.net/forum?id=_hb4vM3jspB)] - CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. [[PUB](https://openreview.net/forum?id=Kf7Yyf4O0u)] [[PDF](https://arxiv.org/abs/2210.02912)] [[CODE](https://github.com/facebookresearch/canife)] - Sparse Random Networks for Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=k1FHgri5y3-)] [[PDF](https://arxiv.org/abs/2209.15328)] [[CODE](https://openreview.net/attachment?id=k1FHgri5y3-&name=SUPP_material)] - Combating Exacerbated Heterogeneity for Robust Decentralized Models. [[PUB](https://openreview.net/forum?id=eKllxpLOOm)] [[CODE](https://github.com/ZFancy/SFAT)] - Hyperparameter Optimization through Neural Network Partitioning. [[PUB](https://openreview.net/forum?id=nAgdXgfmqj)] [[PDF](https://arxiv.org/abs/2304.14766)] - Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?. [[PUB](https://openreview.net/forum?id=2L9gzS80tA4)] [[PDF](https://arxiv.org/abs/2210.10947)] [[CODE](https://openreview.net/attachment?id=2L9gzS80tA4&name=SUPP_material)] - Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. [[PUB](https://openreview.net/forum?id=pfuqQQCB34)] [[PDF](https://arxiv.org/abs/2206.00529)] [[CODE](https://github.com/SamuelHorvath/VR_Byzantine)] - Dual Diffusion Implicit Bridges for Image-to-Image Translation. [[PUB](https://openreview.net/forum?id=5HLoTvVGDe)] [[PDF](https://arxiv.org/abs/2203.08382)] [[CODE](https://openreview.net/attachment?id=5HLoTvVGDe&name=SUPP_material)] - Bias Propagation in Federated Learning. [[PUB](https://openreview.net/pdf?id=V7CYzdruWdm)] - Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. [[PUB](https://openreview.net/pdf?id=eKllxpLOOm)] - Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. [[PUB](https://openreview.net/pdf?id=Mpa3tRJFBb)] - Dataless Knowledge Fusion by Merging Weights of Language Models. [[PUB](https://openreview.net/forum?id=FCnohuR6AnM)] - DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation. [[PUB](https://openreview.net/forum?id=I_YZANaz5X)] - Git Re-Basin: Merging Models modulo Permutation Symmetries. [[PUB](https://openreview.net/forum?id=CQsmMYmlP5T)] - TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. [[PUB](https://openreview.net/forum?id=EIgLnNx_lC)] - Why (and When) does Local SGD Generalize Better than SGD?. [[PUB](https://openreview.net/forum?id=svCcui6Drl)] ### 2022 #### colt - Statistical Estimation and Online Inference via Local SGD. [[PUB](https://proceedings.mlr.press/v178/li22b.html)] #### Mach Learn - An accurate, scalable and verifiable protocol for federated differentially private averaging. [[PUB](https://link.springer.com/article/10.1007/s10994-022-06267-9)] [[PDF](https://arxiv.org/abs/2006.07218)] #### machine learning - An accurate, scalable and verifiable protocol for federated differentially private averaging. [[PUB](https://doi.org/10.1007/s10994-022-06267-9)] #### UAI - Federated online clustering of bandits. [[PUB](https://openreview.net/forum?id=rKUgiU8iqeq)] [[PDF](https://arxiv.org/abs/2208.14865)] [[CODE](https://github.com/zhaohaoru/federated-clustering-of-bandits)] - Privacy-aware compression for federated data analysis. [[PUB](https://openreview.net/forum?id=BqUdRP8i9e9)] [[PDF](https://arxiv.org/abs/2203.08134)] [[CODE](https://github.com/facebookresearch/dp_compression)] - Faster non-convex federated learning via global and local momentum. [[PUB](https://openreview.net/forum?id=SSlLRUIs9e9)] [[PDF](https://arxiv.org/abs/2012.04061)] - Fedvarp: Tackling the variance due to partial client participation in federated learning. [[PUB](https://openreview.net/forum?id=HlWLLdUocx5)] [[PDF](https://arxiv.org/abs/2207.14130)] - SASH: Efficient secure aggregation based on SHPRG for federated learning. [[PUB](https://openreview.net/forum?id=HSleBPIoql9)] [[PDF](https://arxiv.org/abs/2111.12321)] - Bayesian federated estimation of causal effects from observational data. [[PUB](https://openreview.net/forum?id=BEl3vP8sqlc)] [[PDF](https://arxiv.org/abs/2106.00456)] #### TPAMI - Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/9625795)] - Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/9238427)] [[CODE](https://github.com/sunjunaimer/TPAMI-LAQ)] - Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification. [[PUB](https://doi.org/10.1109/TPAMI.2021.3136592)] #### NeurIPS - Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. [[PUB](https://openreview.net/forum?id=W72rB0wwLVu)] [[PDF](https://arxiv.org/abs/2207.03957)] - LAMP: Extracting Text from Gradients with Language Model Priors. [[PUB](https://openreview.net/forum?id=6iqd9JAVR1z)] [[CODE](https://openreview.net/attachment?id=6iqd9JAVR1z&name=SUPP_material)] - FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. [[PUB](https://openreview.net/forum?id=G3fswMh9P8y)] [[PDF](https://arxiv.org/abs/2205.13692)] - On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond. [[PUB](https://openreview.net/forum?id=_33ynl9VgCX)] [[PDF](https://arxiv.org/abs/2206.05187)] - Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. [[PUB](https://openreview.net/forum?id=i9XrHJoyLqJ)] [[CODE](https://openreview.net/attachment?id=i9XrHJoyLqJ&name=SUPP_material)] - Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. [[PUB](https://openreview.net/forum?id=Vj-jYs47cx)] [[PDF](https://arxiv.org/abs/2206.10870)] - Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. [[PUB](https://openreview.net/forum?id=wo-a8Ji6s3A)] - Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. [[PUB](https://openreview.net/forum?id=mUeMOdJ2IJp)] [[PDF](https://arxiv.org/abs/2210.13497)] - EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. [[PUB](https://openreview.net/forum?id=PeJO709WUup)] [[PDF](https://arxiv.org/abs/2205.04180)] - On-Demand Sampling: Learning Optimally from Multiple Distributions. [[PUB](https://openreview.net/forum?id=FR289LMkmxZ)] [[CODE](https://openreview.net/attachment?id=FR289LMkmxZ&name=SUPP_material)] - Improved Utility Analysis of Private CountSketch. [[PUB](https://openreview.net/forum?id=XFCirHGr4Cs)] [[PDF](https://arxiv.org/abs/2205.08397)] [[CODE](https://github.com/rasmus-pagh/private-countsketch)] - Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning. [[PUB](https://openreview.net/forum?id=APXedc0hgdT)] [[CODE](https://openreview.net/attachment?id=APXedc0hgdT&name=SUPP_material)] - Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. [[PUB](https://openreview.net/forum?id=Y4vT7m4e3d)] [[PDF](https://arxiv.org/abs/2106.08315)] - BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. [[PUB](https://openreview.net/forum?id=I47eFCKa1f3)] [[PDF](https://arxiv.org/abs/2201.13320)] [[CODE](https://github.com/liboyue/beer)] - Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. [[PUB](https://openreview.net/forum?id=KOHC_CYEIuP)] [[PDF](https://arxiv.org/abs/2202.06083)] - Near-Optimal Collaborative Learning in Bandits. [[PUB](https://openreview.net/forum?id=2xfJ26BuFP)] [[PDF](https://arxiv.org/abs/2206.00121)] [[CODE](https://github.com/clreda/near-optimal-federated)] - Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. [[PUB](https://openreview.net/forum?id=J0nhRuMkdGf)] [[PDF](https://arxiv.org/abs/2110.03313)] - Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. [[PUB](https://openreview.net/forum?id=SNElc7QmMDe)] [[CODE](https://openreview.net/attachment?id=SNElc7QmMDe&name=SUPP_material)] - FedPop: A Bayesian Approach for Personalised Federated Learning. [[PUB](https://openreview.net/forum?id=KETwimTQexH)] [[PDF](https://arxiv.org/abs/2206.03611)] - Fairness in Federated Learning via Core-Stability. [[PUB](https://openreview.net/forum?id=lKULHf7oFDo)] [[CODE](https://openreview.net/attachment?id=lKULHf7oFDo&name=SUPP_material)] - SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning. [[PUB](https://openreview.net/forum?id=25XIE30VHZE)] [[PDF](https://arxiv.org/abs/2210.01639)] - FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction. [[PUB](https://openreview.net/forum?id=OtxyysUdBE)] [[CODE](https://github.com/MSU-MLSys-Lab/FedRolex)] - On Sample Optimality in Personalized Collaborative and Federated Learning. [[PUB](https://openreview.net/forum?id=7EP90NMAoK)] - DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing. [[PUB](https://openreview.net/forum?id=hPkGV4BPsmv)] [[PDF](https://arxiv.org/abs/2210.02680)] - FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. [[PUB](https://openreview.net/forum?id=5vVSA_cdRqe)] - Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. [[PUB](https://openreview.net/forum?id=edkno3SvKo)] [[PDF](https://arxiv.org/abs/2207.04338)] - VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?. [[PUB](https://openreview.net/forum?id=vNrSXIFJ9wz)] [[CODE](https://openreview.net/attachment?id=edkno3SvKo&name=SUPP_material)] - DENSE: Data-Free One-Shot Federated Learning. [[PUB](https://openreview.net/forum?id=QFQoxCFYEkA)] [[PDF](https://arxiv.org/abs/2112.12371)] - CalFAT: Calibrated Federated Adversarial Training with Label Skewness. [[PUB](https://openreview.net/forum?id=8N1NDRGQSQ)] [[PDF](https://arxiv.org/abs/2205.14926)] - SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning. [[PUB](https://openreview.net/forum?id=wTp4KgVIJ5)] [[PDF](https://arxiv.org/abs/2210.00611)] - Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning. [[PUB](https://openreview.net/forum?id=8SilFGuXgmk)] [[PDF](https://arxiv.org/abs/2210.00690)] - Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. [[PUB](https://openreview.net/forum?id=wFymjzZEEkH)] - Federated Submodel Optimization for Hot and Cold Data Features. [[PUB](https://openreview.net/forum?id=sj9l1JCrAk6)] - BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [[PUB](https://openreview.net/forum?id=jzd2bE5MxW)] [[PDF](https://arxiv.org/abs/2207.06343)] - Byzantine-tolerant federated Gaussian process regression for streaming data. [[PUB](https://openreview.net/forum?id=Nx4gNemvNvx)] [[CODE](https://openreview.net/attachment?id=Nx4gNemvNvx&name=SUPP_material)] - SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. [[PUB](https://openreview.net/forum?id=tz1PRT6lfLe)] [[PDF](https://arxiv.org/abs/2206.09888)] - Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering. [[PUB](https://openreview.net/forum?id=N0tKCpMhA2)] [[PDF](https://arxiv.org/abs/2210.14664)] [[CODE](https://github.com/haoyuzhao123/coreset-vfl-codes)] - Communication Efficient Federated Learning for Generalized Linear Bandits. [[PUB](https://openreview.net/forum?id=Xwz9B6LDM5c)] [[CODE](https://openreview.net/attachment?id=Xwz9B6LDM5c&name=SUPP_material)] - Recovering Private Text in Federated Learning of Language Models. [[PUB](https://openreview.net/forum?id=dqgzfhHd2-)] [[PDF](https://arxiv.org/abs/2205.08514)] [[CODE](https://github.com/Princeton-SysML/FILM)] - Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. [[PUB](https://openreview.net/forum?id=mhQLcMjWw75)] [[PDF](https://arxiv.org/abs/2209.10083)] - Global Convergence of Federated Learning for Mixed Regression. [[PUB](https://openreview.net/forum?id=DdxNka9tMRd)] [[PDF](https://arxiv.org/abs/2206.07279)] - Resource-Adaptive Federated Learning with All-In-One Neural Composition. [[PUB](https://openreview.net/forum?id=wfel7CjOYk)] - Self-Aware Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=EqJ5_hZSqgy)] [[PDF](https://arxiv.org/abs/2204.08069)] - A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning. [[PUB](https://openreview.net/forum?id=TATzsweWfof)] [[PDF](https://arxiv.org/abs/2206.01132)] - An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. [[PUB](https://openreview.net/forum?id=fJt2KFnRqZ)] - Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. [[PUB](https://openreview.net/forum?id=4_oCZgBIVI)] [[PDF](https://arxiv.org/abs/2206.08307)] - Personalized Online Federated Multi-Kernel Learning. [[PUB](https://openreview.net/forum?id=wUctlvhsNWg)] - SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. [[PUB](https://openreview.net/forum?id=1GAjC_FauE)] [[PDF](https://arxiv.org/abs/2106.01432)] [[CODE](https://openreview.net/attachment?id=1GAjC_FauE&name=SUPP_material)] - A Unified Analysis of Federated Learning with Arbitrary Client Participation. [[PUB](https://openreview.net/forum?id=qSs7C7c4G8D)] [[PDF](https://arxiv.org/abs/2205.13648)] - Preservation of the Global Knowledge by Not-True Distillation in Federated Learning. [[PUB](https://openreview.net/forum?id=qw3MZb1Juo)] [[PDF](https://arxiv.org/abs/2106.03097)] [[CODE](https://openreview.net/attachment?id=qw3MZb1Juo&name=SUPP_material)] - FedSR: A Simple and Effective Domain Generalization Method for Federated Learning. [[PUB](https://openreview.net/forum?id=mrt90D00aQX)] [[CODE](https://openreview.net/attachment?id=mrt90D00aQX&name=SUPP_material)] - Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. [[PUB](https://openreview.net/forum?id=Ql75oqz1npy)] [[PDF](https://arxiv.org/abs/2202.00270)] [[CODE](https://github.com/wyjeong/Factorized-FL)] - A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. [[PUB](https://openreview.net/forum?id=Fx7oXUVEPW)] [[PDF](https://arxiv.org/abs/2207.03106)] - Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework. [[PUB](https://openreview.net/forum?id=4OHRr7gmhd4)] - On Privacy and Personalization in Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=Oq2bdIQQOIZ)] [[PDF](https://arxiv.org/abs/2206.07902)] - A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=fiBnhdazkyx)] [[PDF](https://arxiv.org/abs/2106.06312)] [[CODE](https://github.com/Xtra-Computing/FedSim)] - Factorized-FL: Personalized Federated Learning with Parameter Factorization &amp; Similarity Matching. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/e7feb9dbd9a94b6c552fc403fcebf2ef-Abstract-Conference.html)] [[CODE](https://github.com/wyjeong/Factorized-FL)] - FLAIR: Federated Learning Annotated Image Repository. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/f64e55d03e2fe61aa4114e49cb654acb-Abstract-Datasets_and_Benchmarks.html)] - FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets_and_Benchmarks.html)] - Personalized Online Federated Learning with Multiple Kernels. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/d78cc4e15f8fbdb0dd77e551601f572c-Abstract-Conference.html)] - pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/3cc03e19fed71a2b9347d83921ca2e7d-Abstract-Datasets_and_Benchmarks.html)] - TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/c7649eeb93d2fad0ced9a3b974260710-Abstract-Conference.html)] - A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/a7fa0a0d6b4bb14c659b9921e8e4a772-Abstract-Conference.html)] - Collaborative Learning by Detecting Collaboration Partners. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/646ca7b994bc46afe33d680dbe7ed67a-Abstract-Conference.html)] - Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/cf326db238429dac58625977f6fb8265-Abstract-Conference.html)] - Communication Efficient Distributed Learning for Kernelized Contextual Bandits. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/7d1043b688002734b49b766cc2fc478d-Abstract-Conference.html)] - Communication-efficient distributed eigenspace estimation with arbitrary node failures. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/73b038fffc99ae11056e936f9a299508-Abstract-Conference.html)] - GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/4d6938f94ab47d32128c239a4bfedae0-Abstract-Conference.html)] - Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/2616697705f72f16a8eac9c295d37d94-Abstract-Conference.html)] - Merging Models with Fisher-Weighted Averaging. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/70c26937fbf3d4600b69a129031b66ec-Abstract-Conference.html)] - The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/109cf25cbc36037deecdbeabfa199956-Abstract-Conference.html)] - Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/c50c42f853db0f1f5b4195358b6d97de-Abstract-Conference.html)] - SAGDA: Achieving $\mathcal{O}(\epsilon{-2})$ Communication Complexity in Federated Min-Max Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/2f13806d6580db60d9d7d6f89ba529ca-Abstract-Conference.html)] - Taming Fat-Tailed (&quot;Heavier-Tailed&quot; with Potentially Infinite Variance) Noise in Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/6cb7246003d556c4d1cbf9c17c392ee3-Abstract-Conference.html)] #### NeurIPS Datasets and Benchmarks - FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [[PUB](https://openreview.net/forum?id=GgM5DiAb6A2)] [[CODE](https://github.com/owkin/FLamby)] #### ICML - A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. [[PUB](https://proceedings.mlr.press/v162/tan22a.html)] [[PDF](https://arxiv.org/abs/2103.06261)] [[CODE](https://github.com/ellenxtan/ifedtree)] - Fast Composite Optimization and Statistical Recovery in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/bao22b.html)] [[PDF](https://arxiv.org/abs/2207.08204)] [[CODE](https://github.com/MingruiLiu-ML-Lab/Federated-Sparse-Learning)] - Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/bietti22a.html)] [[PDF](https://arxiv.org/abs/2202.05318)] [[CODE](https://github.com/albietz/ppsgd)] - The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning :fire:. [[PUB](https://proceedings.mlr.press/v162/chen22c.html)] [[PDF](https://arxiv.org/abs/2203.03761)] [[CODE](https://github.com/google-research/federated/tree/master/private_linear_compression)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17529.pdf)] - The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. [[PUB](https://proceedings.mlr.press/v162/chen22s.html)] [[PDF](https://arxiv.org/abs/2207.09916)] [[CODE](https://github.com/WeiNingChen/pbm)] - DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. [[PUB](https://proceedings.mlr.press/v162/dai22b.html)] [[PDF](https://arxiv.org/abs/2206.00187)] [[CODE](https://github.com/rong-dai/DisPFL)] - FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/elgabli22a.html)] [[PDF](https://arxiv.org/abs/2206.08829)] [[CODE](https://github.com/aelgabli/FedNew)] - DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. [[PUB](https://proceedings.mlr.press/v162/honig22a.html)] [[PDF](https://arxiv.org/abs/2111.00465)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16009.pdf)] [[CODE](https://media.icml.cc/Conferences/ICML2022/SUPP/honig22a-supp.zip)] - Accelerated Federated Learning with Decoupled Adaptive Optimization. [[PUB](https://proceedings.mlr.press/v162/jin22e.html)] [[PDF](https://arxiv.org/abs/2207.07223)] - Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. [[PUB](https://proceedings.mlr.press/v162/khodadadian22a.html)] [[PDF](https://arxiv.org/abs/2206.10185)] - Multi-Level Branched Regularization for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/kim22a.html)] [[PDF](https://arxiv.org/abs/2207.06936)] [[CODE](https://github.com/jinkyu032/FedMLB)] [[PAGE](http://cvlab.snu.ac.kr/research/FedMLB/)] - FedScale: Benchmarking Model and System Performance of Federated Learning at Scale :fire:. [[PUB](https://proceedings.mlr.press/v162/lai22a.html)] [[PDF](https://arxiv.org/abs/2105.11367)] [[CODE](https://github.com/SymbioticLab/FedScale)] - Federated Learning with Positive and Unlabeled Data. [[PUB](https://proceedings.mlr.press/v162/lin22b.html)] [[PDF](https://arxiv.org/abs/2106.10904)] [[CODE](https://github.com/littlesunlxy/fedpu-torch)] - Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning. [[PUB](https://proceedings.mlr.press/v162/liu22k.html)] [[CODE](https://github.com/Thinklab-SJTU/GAMF)] - Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering. [[PUB](https://proceedings.mlr.press/v162/lubana22a.html)] [[PDF](https://arxiv.org/abs/2205.11506)] [[CODE](https://github.com/akhilmathurs/orchestra)] - Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. [[PUB](https://proceedings.mlr.press/v162/luo22b.html)] [[PDF](https://arxiv.org/abs/2206.06818)] [[CODE](https://github.com/luozhengquan/DFL)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16881.pdf)] [[解读](https://www.bilibili.com/read/cv17092678)] - Architecture Agnostic Federated Learning for Neural Networks. [[PUB](https://proceedings.mlr.press/v162/makhija22a.html)] [[PDF](https://proceedings.mlr.press/v162/zhang22p.html)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16926.pdf)] - Personalized Federated Learning through Local Memorization. [[PUB](https://proceedings.mlr.press/v162/marfoq22a.html)] [[PDF](https://arxiv.org/abs/2111.09360)] [[CODE](https://github.com/omarfoq/knn-per)] - Proximal and Federated Random Reshuffling. [[PUB](https://proceedings.mlr.press/v162/mishchenko22a.html)] [[PDF](https://arxiv.org/abs/2102.06704)] [[CODE](https://github.com/konstmish/rr_prox_fed)] - Federated Learning with Partial Model Personalization. [[PUB](https://proceedings.mlr.press/v162/pillutla22a.html)] [[PDF](https://arxiv.org/abs/2204.03809)] [[CODE](https://github.com/krishnap25/FL_partial_personalization)] - Generalized Federated Learning via Sharpness Aware Minimization. [[PUB](https://proceedings.mlr.press/v162/qu22a.html)] [[PDF](https://arxiv.org/abs/2206.02618)] - FedNL: Making Newton-Type Methods Applicable to Federated Learning. [[PUB](https://proceedings.mlr.press/v162/safaryan22a.html)] [[PDF](https://arxiv.org/abs/2106.02969)] [[VIDEO](https://www.youtube.com/watch?v=_VYCEWT17R0&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17084.pdf)] - Federated Minimax Optimization: Improved Convergence Analyses and Algorithms. [[PUB](https://proceedings.mlr.press/v162/sharma22c.html)] [[PDF](https://arxiv.org/abs/2203.04850)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17435.pdf)] - Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/tang22d.html)] [[PDF](https://arxiv.org/abs/2206.02465)] [[CODE](https://github.com/wizard1203/VHL)] [[解读](https://zhuanlan.zhihu.com/p/548508633)] - FedNest: Federated Bilevel, Minimax, and Compositional Optimization. [[PUB](https://proceedings.mlr.press/v162/tarzanagh22a.html)] [[PDF](https://arxiv.org/abs/2205.02215)] [[CODE](https://github.com/ucr-optml/FedNest)] - EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/vargaftik22a.html)] [[PDF](https://arxiv.org/abs/2108.08842)] [[CODE](https://github.com/amitport/EDEN-Distributed-Mean-Estimation)] - Communication-Efficient Adaptive Federated Learning. [[PUB](https://proceedings.mlr.press/v162/wang22o.html)] [[PDF](https://arxiv.org/abs/2205.02719)] - ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. [[PUB](https://proceedings.mlr.press/v162/wang22y.html)] [[PDF](https://arxiv.org/abs/2110.05323)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16194_hmjFNsN.pdf)] [[CODE](https://github.com/a514514772/ProgFed)] - Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification :fire:. [[PUB](https://proceedings.mlr.press/v162/wen22a.html)] [[PDF](https://arxiv.org/abs/2202.00580)] [[CODE](https://github.com/JonasGeiping/breaching)] - Anarchic Federated Learning. [[PUB](https://proceedings.mlr.press/v162/yang22r.html)] [[PDF](https://arxiv.org/abs/2108.09875)] - QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/yi22a.html)] [[CODE](https://github.com/LipingYi/QSFL)] - Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. [[PUB](https://proceedings.mlr.press/v162/yoon22a.html)] [[PDF](https://arxiv.org/abs/2202.11453)] - Neural Tangent Kernel Empowered Federated Learning. [[PUB](https://proceedings.mlr.press/v162/yue22a.html)] [[PDF](https://arxiv.org/abs/2110.03681)] [[CODE](https://github.com/KAI-YUE/ntk-fed)] - Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. [[PUB](https://proceedings.mlr.press/v162/zhang22b.html)] [[PDF](https://arxiv.org/abs/2106.13673)] - Personalized Federated Learning via Variational Bayesian Inference. [[PUB](https://proceedings.mlr.press/v162/zhang22o.html)] [[PDF](https://arxiv.org/abs/2206.07977)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17302.pdf)] [[UC.](https://github.com/AllenBeau/pFedBayes)] - Federated Learning with Label Distribution Skew via Logits Calibration. [[PUB](https://proceedings.mlr.press/v162/zhang22p.html)] - Neurotoxin: Durable Backdoors in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/zhang22w.html)] [[PDF](https://arxiv.org/abs/2206.10341)] [[CODE](https://github.com/jhcknzzm/Federated-Learning-Backdoor/)] - Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. [[PUB](https://proceedings.mlr.press/v162/zhu22e.html)] - FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. [[PUB](https://proceedings.mlr.press/v162/lai22a.html)] - Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. [[PUB](https://proceedings.mlr.press/v162/wen22a.html)] - The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. [[PUB](https://proceedings.mlr.press/v162/chen22c.html)] - 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. [[PUB](https://proceedings.mlr.press/v162/richtarik22a.html)] - Communication-efficient Distributed Learning for Large Batch Optimization. [[PUB](https://proceedings.mlr.press/v162/liu22n.html)] - PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. [[PUB](https://proceedings.mlr.press/v162/li22s.html)] [[CODE](https://github.com/DIG-Beihang/MIR3)] #### ICLR (oral) - Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [[PUB](https://openreview.net/forum?id=LdlwbBP2mlq)] [[CODE](https://openreview.net/attachment?id=LdlwbBP2mlq&name=SUPP_material)] #### ICLR - Bayesian Framework for Gradient Leakage. [[PUB](https://openreview.net/forum?id=f2lrIbGx3x7)] [[PDF](https://arxiv.org/abs/2111.04706)] [[CODE](https://github.com/eth-sri/bayes-framework-leakage)] - Federated Learning from only unlabeled data with class-conditional-sharing clients. [[PUB](https://openreview.net/forum?id=WHA8009laxu)] [[CODE](https://github.com/lunanbit/FedUL)] - FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning. [[PUB](https://openreview.net/forum?id=ZaVVVlcdaN)] [[PDF](https://arxiv.org/abs/2108.06869.)] - Acceleration of Federated Learning with Alleviated Forgetting in Local Training. [[PUB](https://openreview.net/forum?id=541PxiEKN3F)] [[PDF](https://arxiv.org/abs/2203.02645)] [[CODE](https://github.com/Zoesgithub/FedReg)] - FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=d71n4ftoCBy)] [[PDF](https://arxiv.org/abs/2108.06098)] [[CODE](https://github.com/South-hw/FedPara_ICLR22)] - An Agnostic Approach to Federated Learning with Class Imbalance. [[PUB](https://openreview.net/forum?id=Xo0lbDt975)] [[CODE](https://github.com/shenzebang/Federated-Learning-Pytorch)] - Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization. [[PUB](https://openreview.net/forum?id=_QLmakITKg)] [[PDF](https://arxiv.org/abs/2203.09747)] [[CODE](https://github.com/illidanlab/SplitMix)] - Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models :fire:. [[PUB](https://openreview.net/forum?id=fwzUgo0FM9v)] [[PDF](https://arxiv.org/abs/2110.13057)] [[CODE](https://github.com/JonasGeiping/breaching)] - ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. [[PUB](https://openreview.net/forum?id=2sDQwC_hmnM)] [[PDF](https://arxiv.org/abs/2208.02507)] - Diverse Client Selection for Federated Learning via Submodular Maximization. [[PUB](https://openreview.net/forum?id=nwKXyFvaUm)] [[CODE](https://github.com/melodi-lab/divfl)] - Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?. [[PUB](https://openreview.net/forum?id=B7ZbqNLDn-_)] [[PDF](https://arxiv.org/abs/2202.00280)] [[CODE](https://github.com/shams-sam/FedOptim)] - Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions :fire:. [[PUB](https://openreview.net/forum?id=E4EE_ohFGz)] [[CODE](https://github.com/google-research/federated/tree/7525c36324cb022bc05c3fce88ef01147cae9740/periodic_distribution_shift)] - Towards Model Agnostic Federated Learning Using Knowledge Distillation. [[PUB](https://openreview.net/forum?id=lQI_mZjvBxj)] [[PDF](https://arxiv.org/abs/2110.15210)] [[CODE](https://github.com/AfoninAndrei/ICLR2022)] - Divergence-aware Federated Self-Supervised Learning. [[PUB](https://openreview.net/forum?id=oVE1z8NlNe)] [[PDF](https://arxiv.org/abs/2204.04385)] [[CODE](https://github.com/EasyFL-AI/EasyFL)] - What Do We Mean by Generalization in Federated Learning? :fire:. [[PUB](https://openreview.net/forum?id=VimqQq-i_Q)] [[PDF](https://arxiv.org/abs/2110.14216)] [[CODE](https://github.com/google-research/federated/tree/master/generalization)] - FedBABU: Toward Enhanced Representation for Federated Image Classification. [[PUB](https://openreview.net/forum?id=HuaYQfggn5u)] [[PDF](https://arxiv.org/abs/2106.06042)] [[CODE](https://github.com/jhoon-oh/FedBABU)] - Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. [[PUB](https://openreview.net/forum?id=jXKKDEi5vJt)] [[PDF](https://arxiv.org/abs/2006.09365)] [[CODE](https://github.com/liehe/byzantine-robust-noniid-optimizer)] - Hybrid Local SGD for Federated Learning with Heterogeneous Communications. [[PUB](https://openreview.net/forum?id=H0oaWl6THa)] - On Bridging Generic and Personalized Federated Learning for Image Classification. [[PUB](https://openreview.net/forum?id=I1hQbx10Kxn)] [[PDF](https://arxiv.org/abs/2107.00778)] [[CODE](https://github.com/hongyouc/Fed-RoD)] - Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [[PUB](https://openreview.net/forum?id=LdlwbBP2mlq)] [[PDF](https://arxiv.org/abs/2110.10342)] - Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. [[PUB](https://openreview.net/forum?id=E4EE_ohFGz)] - FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=d71n4ftoCBy)] - Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [[PUB](https://openreview.net/forum?id=7l1IjZVddDW)] - Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. [[PUB](https://openreview.net/forum?id=fwzUgo0FM9v)] - What Do We Mean by Generalization in Federated Learning?. [[PUB](https://openreview.net/forum?id=VimqQq-i_Q)] - SGD Can Converge to Local Maxima. [[PUB](https://openreview.net/forum?id=9XhPLAjjRB)] #### ICLR Spotlight - Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [[PUB](https://openreview.net/forum?id=7l1IjZVddDW)] [[PDF](https://arxiv.org/abs/2201.12467)] [[PAGE](https://irvingmeng.github.io/projects/privacyface/)] [[解读](https://zhuanlan.zhihu.com/p/484920301)] ### 2021 #### JMLR - One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. [[PUB](http://jmlr.org/papers/v22/19-1048.html)] [[CODE](https://github.com/sabersalehk/MRE_C)] - Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA. [[PUB](https://jmlr.org/papers/v22/20-705.html)] - Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms. [[PUB](https://jmlr.org/papers/v22/20-147.html)] - Estimating Uncertainty Intervals from Collaborating Networks. [[PUB](https://jmlr.org/papers/v22/20-1100.html)] - FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection. [[PUB](https://jmlr.org/papers/v22/20-815.html)] - Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. [[PUB](https://jmlr.org/papers/v22/19-325.html)] #### tpami - Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction. [[PUB](https://doi.org/10.1109/TPAMI.2020.2991344)] #### UAI - Constrained differentially private federated learning for low-bandwidth devices. [[PUB](https://proceedings.mlr.press/v161/kerkouche21a.html)] [[PDF](https://arxiv.org/abs/2103.00342)] - Federated stochastic gradient Langevin dynamics. [[PUB](https://proceedings.mlr.press/v161/mekkaoui21a.html)] [[PDF](https://arxiv.org/abs/2004.11231)] #### ICLR - Federated Learning Based on Dynamic Regularization. [[PUB](https://openreview.net/forum?id=B7v4QMR6Z9w)] [[PDF](https://arxiv.org/abs/2111.04263)] [[CODE](https://github.com/AntixK/FedDyn)] - Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning. [[PUB](https://openreview.net/forum?id=jDdzh5ul-d)] [[PDF](https://arxiv.org/abs/2101.11203)] - HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=TNkPBBYFkXg)] [[PDF](https://arxiv.org/abs/2010.01264)] [[CODE](https://github.com/dem123456789/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients)] - FedMix: Approximation of Mixup under Mean Augmented Federated Learning. [[PUB](https://openreview.net/forum?id=Ogga20D2HO-)] [[PDF](https://arxiv.org/abs/2107.00233)] - Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms :fire:. [[PUB](https://openreview.net/forum?id=GFsU8a0sGB)] [[PDF](https://arxiv.org/abs/2010.05273)] [[CODE](https://github.com/alshedivat/fedpa)] - Adaptive Federated Optimization :fire:. [[PUB](https://openreview.net/forum?id=LkFG3lB13U5)] [[PDF](https://arxiv.org/abs/2003.00295)] [[CODE](https://github.com/google-research/federated/tree/master/optimization)] - Personalized Federated Learning with First Order Model Optimization. [[PUB](https://openreview.net/forum?id=ehJqJQk9cw)] [[PDF](https://arxiv.org/abs/2012.08565)] [[CODE](https://github.com/NVlabs/FedFomo)] [[UC.](https://github.com/TsingZ0/PFL-Non-IID)] - FedBN: Federated Learning on Non-IID Features via Local Batch Normalization :fire:. [[PUB](https://openreview.net/forum?id=6YEQUn0QICG)] [[PDF](https://arxiv.org/abs/2102.07623)] [[CODE](https://github.com/med-air/FedBN)] - FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning. [[PUB](https://openreview.net/forum?id=dgtpE6gKjHn)] [[PDF](https://arxiv.org/abs/2009.01974)] [[CODE](https://github.com/hongyouc/fedbe)] - Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. [[PUB](https://openreview.net/forum?id=ce6CFXBh30h)] [[PDF](https://arxiv.org/abs/2006.12097)] [[CODE](https://github.com/wyjeong/FedMatch)] - Adaptive Federated Optimization. [[PUB](https://openreview.net/forum?id=LkFG3lB13U5)] - FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. [[PUB](https://openreview.net/forum?id=6YEQUn0QICG)] [[CODE](https://github.com/med-air/FedBN)] - Federated Semi-Supervised Learning with Inter-Client Consistency &amp; Disjoint Learning. [[PUB](https://openreview.net/forum?id=ce6CFXBh30h)] [[CODE](https://github.com/wyjeong/FedMatch)] - A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. [[PUB](https://openreview.net/forum?id=vYVI1CHPaQg)] - CaPC Learning: Confidential and Private Collaborative Learning. [[PUB](https://openreview.net/forum?id=h2EbJ4_wMVq)] - Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks. [[PUB](https://openreview.net/forum?id=C70cp4Cn32)] - Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. [[PUB](https://openreview.net/forum?id=GFsU8a0sGB)] #### ICML - KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. [[PUB](http://proceedings.mlr.press/v139/feng21f.html)] [[PDF](https://arxiv.org/abs/2011.09757)] [[CODE](https://github.com/FengHZ/KD3A)] [[解读](https://mp.weixin.qq.com/s/gItgiZmKUxg0ltaeOVdnRw)] - Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. [[PUB](http://proceedings.mlr.press/v139/lam21b.html)] [[PDF](https://arxiv.org/abs/2106.06089)] [[VIDEO](https://slideslive.com/38958558/gradient-disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix)] [[CODE](https://github.com/gdisag/gradient_disaggregation)] - FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. [[PUB](http://proceedings.mlr.press/v139/huang21c.html)] [[PDF](https://arxiv.org/abs/2105.05001)] [[VIDEO](https://slideslive.com/38959650/flntk-a-neural-tangent-kernelbased-framework-for-federated-learning-analysis)] - Personalized Federated Learning using Hypernetworks :fire:. [[PUB](http://proceedings.mlr.press/v139/shamsian21a.html)] [[PDF](https://arxiv.org/abs/2103.04628)] [[CODE](https://github.com/AvivSham/pFedHN)] [[PAGE](https://avivsham.github.io/pfedhn/)] [[VIDEO](https://slideslive.com/38959583/personalized-federated-learning-using-hypernetworks)] [[解读](https://zhuanlan.zhihu.com/p/431130945)] - Federated Composite Optimization. [[PUB](http://proceedings.mlr.press/v139/yuan21d.html)] [[PDF](https://arxiv.org/abs/2011.08474)] [[CODE](https://github.com/hongliny/FCO-ICML21)] [[VIDEO](https://www.youtube.com/watch?v=tKDbc60XJks&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https://hongliny.github.io/files/FCO_ICML21/FCO_ICML21_slides.pdf)] - Exploiting Shared Representations for Personalized Federated Learning. [[PUB](http://proceedings.mlr.press/v139/collins21a.html)] [[PDF](https://arxiv.org/abs/2102.07078)] [[CODE](https://github.com/lgcollins/FedRep)] [[VIDEO](https://slideslive.com/38959519/exploiting-shared-representations-for-personalized-federated-learning)] - Data-Free Knowledge Distillation for Heterogeneous Federated Learning :fire:. [[PUB](http://proceedings.mlr.press/v139/zhu21b.html)] [[PDF](https://arxiv.org/abs/2105.10056)] [[CODE](https://github.com/zhuangdizhu/FedGen)] [[VIDEO](https://slideslive.com/38959429/datafree-knowledge-distillation-for-heterogeneous-federated-learning)] - Federated Continual Learning with Weighted Inter-client Transfer. [[PUB](http://proceedings.mlr.press/v139/yoon21b.html)] [[PDF](https://arxiv.org/abs/2003.03196)] [[CODE](https://github.com/wyjeong/FedWeIT)] [[VIDEO](https://slideslive.com/38959323/federated-continual-learning-with-weighted-interclient-transfer)] - Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. [[PUB](http://proceedings.mlr.press/v139/yuan21a.html)] [[PDF](https://arxiv.org/abs/2102.04635)] [[CODE](https://libauc.org/)] [[VIDEO](https://slideslive.com/38959235/federated-deep-auc-maximization-for-hetergeneous-data-with-a-constant-communication-complexity)] - Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. [[PUB](http://proceedings.mlr.press/v139/murata21a.html)] [[PDF](https://arxiv.org/abs/2102.03198)] [[VIDEO](https://slideslive.com/38959169/biasvariance-reduced-local-sgd-for-less-heterogeneous-federated-learning)] - Federated Learning of User Verification Models Without Sharing Embeddings. [[PUB](http://proceedings.mlr.press/v139/hosseini21a.html)] [[PDF](https://arxiv.org/abs/2104.08776)] [[VIDEO](https://slideslive.com/38958858/federated-learning-of-user-verification-models-without-sharing-embeddings)] - Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. [[PUB](http://proceedings.mlr.press/v139/fraboni21a.html)] [[PDF](https://arxiv.org/abs/2105.05883)] [[CODE](https://github.com/Accenture//Labs-Federated-Learning/tree/clustered_sampling)] [[VIDEO](https://slideslive.com/38959618/clustered-sampling-lowvariance-and-improved-representativity-for-clients-selection-in-federated-learning)] - Ditto: Fair and Robust Federated Learning Through Personalization. [[PUB](http://proceedings.mlr.press/v139/li21h.html)] [[PDF](https://arxiv.org/abs/2012.04221)] [[CODE](https://github.com/litian96/ditto)] [[VIDEO](https://slideslive.com/38955195/ditto-fair-and-robust-federated-learning-through-personalization)] - Heterogeneity for the Win: One-Shot Federated Clustering. [[PUB](http://proceedings.mlr.press/v139/dennis21a.html)] [[PDF](https://arxiv.org/abs/2103.00697)] [[VIDEO](https://slideslive.com/38959380/heterogeneity-for-the-win-oneshot-federated-clustering)] - The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation :fire:. [[PUB](http://proceedings.mlr.press/v139/kairouz21a.html)] [[PDF](https://arxiv.org/abs/2102.06387)] [[CODE](https://github.com/google-research/federated/tree/master/distributed_dp)] [[VIDEO](https://slideslive.com/38959306/the-distributed-discrete-gaussian-mechanism-for-federated-learning-with-secure-aggregation)] - Debiasing Model Updates for Improving Personalized Federated Training. [[PUB](http://proceedings.mlr.press/v139/acar21a.html)] [[CODE](https://github.com/venkatesh-saligrama/Personalized-Federated-Learning)] [[VIDEO](https://slideslive.com/38959212/debiasing-model-updates-for-improving-personalized-federated-training)] - One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. [[PUB](http://proceedings.mlr.press/v139/blum21a.html)] [[PDF](https://arxiv.org/abs/2103.03228)] [[CODE](https://github.com/rlphilli/Collaborative-Incentives)] [[VIDEO](https://slideslive.com/38959135/one-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning)] - CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. [[PUB](http://proceedings.mlr.press/v139/xie21a.html)] [[PDF](https://arxiv.org/abs/2106.08283)] [[CODE](https://github.com/AI-secure/CRFL)] [[VIDEO](https://slideslive.com/38959047/crfl-certifiably-robust-federated-learning-against-backdoor-attacks)] - Federated Learning under Arbitrary Communication Patterns. [[PUB](http://proceedings.mlr.press/v139/avdiukhin21a.html)] [[VIDEO](https://slideslive.com/38959048/federated-learning-under-arbitrary-communication-patterns)] - Data-Free Knowledge Distillation for Heterogeneous Federated Learning. [[PUB](http://proceedings.mlr.press/v139/zhu21b.html)] - Personalized Federated Learning using Hypernetworks. [[PUB](http://proceedings.mlr.press/v139/shamsian21a.html)] - The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. [[PUB](http://proceedings.mlr.press/v139/kairouz21a.html)] - Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. [[PUB](http://proceedings.mlr.press/v139/data21a.html)] - Communication-Efficient Distributed Optimization with Quantized Preconditioners. [[PUB](http://proceedings.mlr.press/v139/alimisis21a.html)] - Communication-Efficient Distributed SVD via Local Power Iterations. [[PUB](http://proceedings.mlr.press/v139/li21u.html)] - Matrix Sketching for Secure Collaborative Machine Learning. [[PUB](http://proceedings.mlr.press/v139/zhang21v.html)] #### NeurIPS - CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. [[PUB](https://openreview.net/forum?id=eNB4WXnNczJ)] [[PDF](https://arxiv.org/abs/2107.09461)] - Boosting with Multiple Sources. [[PUB](https://openreview.net/forum?id=1oP1duoZxx)] - DRIVE: One-bit Distributed Mean Estimation. [[PUB](https://openreview.net/forum?id=KXRTmcv3dQ8)] [[CODE](https://github.com/amitport/DRIVE-One-bit-Distributed-Mean-Estimation)] - Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. [[PUB](https://openreview.net/forum?id=yRfsADObu18)] [[CODE](https://github.com/XinyiYS/Gradient-Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning)] - Gradient Inversion with Generative Image Prior. [[PUB](https://papers.nips.cc/paper/2021/hash/fa84632d742f2729dc32ce8cb5d49733-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14962)] [[CODE](https://github.com/ml-postech/gradient-inversion-generative-image-prior)] - Distributed Machine Learning with Sparse Heterogeneous Data. [[PUB](https://openreview.net/forum?id=F9HNBbytcqT)] [[PDF](https://arxiv.org/abs/1912.01417)] - Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. [[PUB](https://openreview.net/forum?id=SPrVNsXnGd)] [[PDF](https://arxiv.org/abs/2107.08763)] - Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/076a8133735eb5d7552dc195b125a454-Abstract.html)] - CAFE: Catastrophic Data Leakage in Vertical Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/08040837089cdf46631a10aca5258e16-Abstract.html)] [[CODE](https://github.com/DeRafael/CAFE)] - Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. [[PUB](https://papers.nips.cc/paper/2021/hash/080acdcce72c06873a773c4311c2e464-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14074)] [[CODE](https://github.com/flint-xf-fan/Byzantine-Federeated-RL)] - Optimality and Stability in Federated Learning: A Game-theoretic Approach. [[PUB](https://papers.nips.cc/paper/2021/hash/09a5e2a11bea20817477e0b1dfe2cc21-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.09580)] [[CODE](https://github.com/kpdonahue/model_sharing_games)] - QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/1dba3025b159cd9354da65e2d0436a31-Abstract.html)] [[PDF](https://arxiv.org/abs/2107.13892)] [[CODE](https://github.com/zkhku/fedsage)] [[解读](https://zhuanlan.zhihu.com/p/430789355)] - The Skellam Mechanism for Differentially Private Federated Learning :fire:. [[PUB](https://papers.neurips.cc/paper/2021/hash/285baacbdf8fda1de94b19282acd23e2-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.04995)] [[CODE](https://github.com/google-research/federated/tree/master/distributed_dp)] - No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. [[PUB](https://papers.neurips.cc/paper/2021/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.05001)] - STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. [[PUB](https://papers.neurips.cc/paper/2021/hash/3016a447172f3045b65f5fc83e04b554-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.10435)] - Subgraph Federated Learning with Missing Neighbor Generation. [[PUB](https://papers.neurips.cc/paper/2021/hash/34adeb8e3242824038aa65460a47c29e-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.13430)] [[CODE](https://github.com/zkhku/fedsage)] [[解读](https://zhuanlan.zhihu.com/p/423555171)] - Evaluating Gradient Inversion Attacks and Defenses in Federated Learning :fire:. [[PUB](https://papers.neurips.cc/paper/2021/hash/3b3fff6463464959dcd1b68d0320f781-Abstract.html)] [[PDF](https://arxiv.org/abs/2112.00059)] [[CODE](https://github.com/Princeton-SysML/GradAttack)] - Personalized Federated Learning With Gaussian Processes. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/46d0671dd4117ea366031f87f3aa0093-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.15482)] [[CODE](https://github.com/IdanAchituve/pFedGP)] - Differentially Private Federated Bayesian Optimization with Distributed Exploration. [[PUB](https://papers.nips.cc/paper/2021/hash/4c27cea8526af8cfee3be5e183ac9605-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14153)] [[CODE](https://github.com/daizhongxiang/Differentially-Private-Federated-Bayesian-Optimization)] - Parameterized Knowledge Transfer for Personalized Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/5383c7318a3158b9bc261d0b6996f7c2-Abstract.html)] [[PDF](https://arxiv.org/abs/2111.02862)] [[CODE](https://github.com/cugzj/KT-pFL)] - Federated Reconstruction: Partially Local Federated Learning :fire:. [[PUB](https://papers.nips.cc/paper/2021/hash/5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.03448)] [[CODE](https://github.com/google-research/federated/tree/master/reconstruction)] [[UC.](https://github.com/KarhouTam/FedRecon)] - Fast Federated Learning in the Presence of Arbitrary Device Unavailability. [[PUB](https://papers.nips.cc/paper/2021/hash/64be20f6dd1dd46adf110cf871e3ed35-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.04159)] [[CODE](https://github.com/hmgxr128/MIFA_code/)] - FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. [[PUB](https://papers.nips.cc/paper/2021/hash/692baebec3bb4b53d7ebc3b9fabac31b-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.13864)] [[CODE](https://github.com/jeremy313/FL-WBC)] - FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. [[PUB](https://papers.nips.cc/paper/2021/hash/6aed000af86a084f9cb0264161e29dd3-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.13451)] - Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients. [[PUB](https://papers.nips.cc/paper/2021/hash/7a6bda9ad6ffdac035c752743b7e9d0e-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.07053)] [[VIDEO](https://papertalk.org/papertalks/35898)] - Federated Multi-Task Learning under a Mixture of Distributions. [[PUB](https://papers.nips.cc/paper/2021/hash/82599a4ec94aca066873c99b4c741ed8-Abstract.html)] [[PDF](https://arxiv.org/abs/2108.10252)] [[CODE](https://github.com/omarfoq/FedEM)] - Federated Graph Classification over Non-IID Graphs. [[PUB](https://papers.nips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.13423)] [[CODE](https://github.com/Oxfordblue7/GCFL)] [[解读](https://zhuanlan.zhihu.com/p/430718887)] - Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. [[PUB](https://papers.nips.cc/paper/2021/hash/a0205b87490c847182672e8d371e9948-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.04502)] [[CODE](https://github.com/mkhodak/fedex)] - On Large-Cohort Training for Federated Learning :fire:. [[PUB](https://papers.nips.cc/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.07820)] [[CODE](https://github.com/google-research/federated/tree/f4e26c1b9b47ac320e520a8b9943ea2c5324b8c2/large_cohort)] - DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/b0ab42fcb7133122b38521d13da7120b-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.03112)] [[CODE](https://github.com/hangxu0304/DeepReduce)] - PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization. [[PUB](https://papers.nips.cc/paper/2021/hash/c429429bf1f2af051f2021dc92a8ebea-Abstract.html)] [[VIDEO](https://papertalk.org/papertalks/37327)] - Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis. [[PUB](https://papers.nips.cc/paper/2021/hash/ceb0595112db2513b9325a85761b7310-Abstract.html)] [[PDF](https://arxiv.org/abs/2111.01338)] - Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/db8e1af0cb3aca1ae2d0018624204529-Abstract.html)] [[PDF](https://arxiv.org/abs/2108.08435)] [[CODE](https://github.com/cuis15/FCFL)] - Federated Linear Contextual Bandits. [[PUB](https://papers.nips.cc/paper/2021/hash/e347c51419ffb23ca3fd5050202f9c3d-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14177)] [[CODE](https://github.com/Ruiquan5514/Federated-Linear-Contextual-Bandits)] - Few-Round Learning for Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/f065d878ccfb4cc4f4265a4ff8bafa9a-Abstract.html)] - Breaking the centralized barrier for cross-device federated learning. [[PUB](https://papers.nips.cc/paper/2021/hash/f0e6be4ce76ccfa73c5a540d992d0756-Abstract.html)] [[CODE](https://fedjax.readthedocs.io/en/latest/fedjax.algorithms.html#module-fedjax.algorithms.mime)] [[VIDEO](https://papertalk.org/papertalks/37564)] - Federated-EM with heterogeneity mitigation and variance reduction. [[PUB](https://papers.nips.cc/paper/2021/hash/f740c8d9c193f16d8a07d3a8a751d13f-Abstract.html)] [[PDF](https://arxiv.org/abs/2111.02083)] - Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/fc03d48253286a798f5116ec00e99b2b-Abstract.html)] [[PAGE](https://dga.hanlab.ai/)] [[SLIDE](https://dga.hanlab.ai/assets/dga_slides.pdf)] - FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. [[PUB](https://papers.nips.cc/paper/2021/hash/fe7ee8fc1959cc7214fa21c4840dff0a-Abstract.html)] [[PDF](https://arxiv.org/abs/2103.03452)] [[CODE](https://github.com/unc-optimization/FedDR)] - Catastrophic Data Leakage in Vertical Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/08040837089cdf46631a10aca5258e16-Abstract.html)] - Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/3b3fff6463464959dcd1b68d0320f781-Abstract.html)] - Federated Reconstruction: Partially Local Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)] - On Large-Cohort Training for Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html)] - The Skellam Mechanism for Differentially Private Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/285baacbdf8fda1de94b19282acd23e2-Abstract.html)] - Asynchronous Decentralized SGD with Quantized and Local Updates. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/362c99307cdc3f2d8b410652386a9dd1-Abstract.html)] - CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/56577889b3c1cd083b6d7b32d32f99d5-Abstract.html)] - Communication-efficient SGD: From Local SGD to One-Shot Averaging. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/cc06a6150b92e17dd3076a0f0f9d2af4-Abstract.html)] - Distributed Deep Learning In Open Collaborations. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/41a60377ba920919939d83326ebee5a1-Abstract.html)] - Learning Collaborative Policies to Solve NP-hard Routing Problems. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/564127c03caab942e503ee6f810f54fd-Abstract.html)] - Learning Distilled Collaboration Graph for Multi-Agent Perception. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/f702defbc67edb455949f46babab0c18-Abstract.html)] - Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/5c53292c032b6cb8510041c54274e65f-Abstract.html)] - Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning). [[PUB](https://proceedings.neurips.cc/paper/2021/hash/d2cd33e9c0236a8c2d8bd3fa91ad3acf-Abstract.html)] ### 2020 #### ICLR - Federated Adversarial Domain Adaptation. [[PUB](https://openreview.net/forum?id=HJezF3VYPB)] [[PDF](https://arxiv.org/abs/1911.02054)] [[CODE](https://drive.google.com/file/d/1OekTpqB6qLfjlE2XUjQPm3F110KDMFc0/view?usp=sharing)] - DBA: Distributed Backdoor Attacks against Federated Learning. [[PUB](https://openreview.net/forum?id=rkgyS0VFvr)] [[CODE](https://github.com/AI-secure/DBA)] - Fair Resource Allocation in Federated Learning :fire:. [[PUB](https://openreview.net/forum?id=ByexElSYDr)] [[PDF](https://arxiv.org/abs/1905.10497)] [[CODE](https://github.com/litian96/fair_flearn)] - Federated Learning with Matched Averaging :fire:. [[PUB](https://openreview.net/forum?id=BkluqlSFDS)] [[PDF](https://arxiv.org/abs/2002.06440)] [[CODE](https://github.com/IBM/FedMA)] - Differentially Private Meta-Learning. [[PUB](https://openreview.net/forum?id=rJgqMRVYvr)] [[PDF](https://proceedings.mlr.press/v162/zhang22p.html)] - Generative Models for Effective ML on Private, Decentralized Datasets :fire:. [[PUB](https://openreview.net/forum?id=SJgaRA4FPH)] [[PDF](https://arxiv.org/abs/1911.06679)] [[CODE](https://github.com/google-research/federated/tree/master/gans)] - On the Convergence of FedAvg on Non-IID Data :fire:. [[PUB](https://openreview.net/forum?id=HJxNAnVtDS)] [[PDF](https://arxiv.org/abs/1907.02189)] [[CODE](https://github.com/lx10077/fedavgpy)] [[解读](https://zhuanlan.zhihu.com/p/500005337)] - Fair Resource Allocation in Federated Learning. [[PUB](https://openreview.net/forum?id=ByexElSYDr)] - Federated Learning with Matched Averaging. [[PUB](https://openreview.net/forum?id=BkluqlSFDS)] - Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. [[PUB](https://openreview.net/forum?id=SJxZnR4YvB)] - Don&apos;t Use Large Mini-batches, Use Local SGD. [[PUB](https://openreview.net/forum?id=B1eyO1BFPr)] - On the Convergence of FedAvg on Non-IID Data. [[PUB](https://openreview.net/forum?id=HJxNAnVtDS)] - SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. [[PUB](https://openreview.net/forum?id=SkxJ8REYPH)] #### ICML - FedBoost: A Communication-Efficient Algorithm for Federated Learning. [[PUB](http://proceedings.mlr.press/v119/hamer20a.html)] [[VIDEO](https://slideslive.com/38928463/fedboost-a-communicationefficient-algorithm-for-federated-learning?ref=speaker-16993-latest)] - FetchSGD: Communication-Efficient Federated Learning with Sketching. [[PUB](http://proceedings.mlr.press/v119/rothchild20a.html)] [[PDF](https://arxiv.org/abs/2007.07682)] [[VIDEO](https://slideslive.com/38928454/fetchsgd-communicationefficient-federated-learning-with-sketching)] [[CODE](https://github.com/kiddyboots216/CommEfficient)] - SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. [[PUB](http://proceedings.mlr.press/v119/karimireddy20a.html)] [[PDF](https://arxiv.org/abs/1910.06378)] [[VIDEO](https://slideslive.com/38927610/scaffold-stochastic-controlled-averaging-for-federated-learning)] [[UC.](https://github.com/ramshi236/Accelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks)] [[解读](https://zhuanlan.zhihu.com/p/538941775)] - Federated Learning with Only Positive Labels. [[PUB](http://proceedings.mlr.press/v119/yu20f.html)] [[PDF](https://arxiv.org/abs/2004.10342)] [[VIDEO](https://slideslive.com/38928322/federated-learning-with-only-positive-labels)] - From Local SGD to Local Fixed-Point Methods for Federated Learning. [[PUB](http://proceedings.mlr.press/v119/malinovskiy20a.html)] [[PDF](https://arxiv.org/abs/2004.01442)] [[SLIDE](https://icml.cc/media/Slides/icml/2020/virtual)] [[VIDEO](https://slideslive.com/38928320/from-local-sgd-to-local-fixed-point-methods-for-federated-learning)] - Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. [[PUB](http://proceedings.mlr.press/v119/li20g.html)] [[PDF](https://arxiv.org/abs/2002.11364)] [[SLIDE](https://icml.cc/media/Slides/icml/2020/virtual)] [[VIDEO](https://slideslive.com/38927921/acceleration-for-compressed-gradient-descent-in-distributed-optimization)] - A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. [[PUB](http://proceedings.mlr.press/v119/koloskova20a.html)] - Collaborative Machine Learning with Incentive-Aware Model Rewards. [[PUB](http://proceedings.mlr.press/v119/sim20a.html)] - Communication-Efficient Distributed PCA by Riemannian Optimization. [[PUB](http://proceedings.mlr.press/v119/huang20e.html)] - Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. [[PUB](http://proceedings.mlr.press/v119/guo20f.html)] - Is Local SGD Better than Minibatch SGD?. [[PUB](http://proceedings.mlr.press/v119/woodworth20a.html)] - Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. [[PUB](http://proceedings.mlr.press/v119/li20b.html)] #### jmlr - GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning. [[PUB](https://jmlr.org/papers/v21/19-718.html)] #### machine learning - Communication-efficient distributed multi-task learning with matrix sparsity regularization. [[PUB](https://doi.org/10.1007/s10994-019-05847-6)] #### NeurIPS - Differentially-Private Federated Linear Bandits. [[PUB](https://papers.nips.cc/paper/2020/hash/4311359ed4969e8401880e3c1836fbe1-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.11425)] [[CODE](https://github.com/abhimanyudubey/private_federated_linear_bandits)] - Federated Principal Component Analysis. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/47a658229eb2368a99f1d032c8848542-Abstract.html)] [[PDF](https://arxiv.org/abs/1907.08059)] [[CODE](https://github.com/andylamp/federated_pca)] - FedSplit: an algorithmic framework for fast federated optimization. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/4ebd440d99504722d80de606ea8507da-Abstract.html)] [[PDF](https://arxiv.org/abs/2005.05238)] - Federated Bayesian Optimization via Thompson Sampling. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/6dfe08eda761bd321f8a9b239f6f4ec3-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.10154)] [[CODE](https://github.com/daizhongxiang/Federated_Bayesian_Optimization)] - Lower Bounds and Optimal Algorithms for Personalized Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/187acf7982f3c169b3075132380986e4-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.02372)] - Robust Federated Learning: The Case of Affine Distribution Shifts. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/f5e536083a438cec5b64a4954abc17f1-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.08907)] [[CODE](https://github.com/farzanfarnia/RobustFL)] - An Efficient Framework for Clustered Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/e32cc80bf07915058ce90722ee17bb71-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.04088)] [[CODE](https://github.com/jichan3751/ifca)] - Distributionally Robust Federated Averaging :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/ac450d10e166657ec8f93a1b65ca1b14-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.12660)] [[CODE](https://github.com/MLOPTPSU/FedTorch)] - Personalized Federated Learning with Moreau Envelopes :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/f4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.08848)] [[CODE](https://github.com/CharlieDinh/pFedMe)] - Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/24389bfe4fe2eba8bf9aa9203a44cdad-Abstract.html)] [[PDF](https://arxiv.org/abs/2002.07948)] [[UC.](https://github.com/KarhouTam/Per-FedAvg)] - Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/a1d4c20b182ad7137ab3606f0e3fc8a4-Abstract.html)] [[PDF](https://arxiv.org/abs/2007.14513)] [[CODE](https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/distributed/fedgkt)] [[解读](https://zhuanlan.zhihu.com/p/536901871)] - Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/564127c03caab942e503ee6f810f54fd-Abstract.html)] [[PDF](https://arxiv.org/abs/2007.07481)] [[CODE](https://github.com/JYWa/FedNova)] [[UC.](https://github.com/carbonati/fl-zoo)] - Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/b8ffa41d4e492f0fad2f13e29e1762eb-Abstract.html)] [[PDF](https://arxiv.org/abs/2007.05084)] - Federated Accelerated Stochastic Gradient Descent. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/39d0a8908fbe6c18039ea8227f827023-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.08950)] [[CODE](https://github.com/hongliny/FedAc-NeurIPS20)] [[VIDEO](https://youtu.be/K28zpAgg3HM)] - Inverting Gradients - How easy is it to break privacy in federated learning? :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html)] [[PDF](https://arxiv.org/abs/2003.14053)] [[CODE](https://github.com/JonasGeiping/invertinggradients)] - Ensemble Distillation for Robust Model Fusion in Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/18df51b97ccd68128e994804f3eccc87-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.07242)] [[CODE](https://github.com/epfml/federated-learning-public-code/tree/master/codes/FedDF-code)] - Throughput-Optimal Topology Design for Cross-Silo Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/e29b722e35040b88678e25a1ec032a21-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.12229)] [[CODE](https://github.com/omarfoq/communication-in-cross-silo-fl)] - Distributionally Robust Federated Averaging. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/ac450d10e166657ec8f93a1b65ca1b14-Abstract.html)] - Inverting Gradients - How easy is it to break privacy in federated learning?. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html)] - Personalized Federated Learning with Moreau Envelopes. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/f4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html)] - Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/564127c03caab942e503ee6f810f54fd-Abstract.html)] - A Scalable Approach for Privacy-Preserving Collaborative Machine Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/5bf8aaef51c6e0d363cbe554acaf3f20-Abstract.html)] - Minibatch vs Local SGD for Heterogeneous Distributed Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/45713f6ff2041d3fdfae927b82488db8-Abstract.html)] - ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/9d58963592071dbf38a0fa114269959c-Abstract.html)] ### 2019 #### colt - Communication and Memory Efficient Testing of Discrete Distributions. [[PUB](http://proceedings.mlr.press/v99/diakonikolas19a.html)] #### iclr - Local SGD Converges Fast and Communicates Little. [[PUB](https://openreview.net/forum?id=S1g2JnRcFX)] #### ICML - Bayesian Nonparametric Federated Learning of Neural Networks :fire:. [[PUB](http://proceedings.mlr.press/v97/yurochkin19a.html)] [[PDF](https://arxiv.org/abs/1905.12022)] [[CODE](https://github.com/IBM/probabilistic-federated-neural-matching)] - Analyzing Federated Learning through an Adversarial Lens :fire:. [[PUB](http://proceedings.mlr.press/v97/bhagoji19a.html)] [[PDF](https://arxiv.org/abs/1811.12470)] [[CODE](https://github.com/inspire-group/ModelPoisoning)] - Agnostic Federated Learning. [[PUB](http://proceedings.mlr.press/v97/mohri19a.html)] [[PDF](https://arxiv.org/abs/1902.00146)] - Analyzing Federated Learning through an Adversarial Lens. [[PUB](http://proceedings.mlr.press/v97/bhagoji19a.html)] - Bayesian Nonparametric Federated Learning of Neural Networks. [[PUB](http://proceedings.mlr.press/v97/yurochkin19a.html)] - Collaborative Evolutionary Reinforcement Learning. [[PUB](http://proceedings.mlr.press/v97/khadka19a.html)] - Learning to Collaborate in Markov Decision Processes. [[PUB](http://proceedings.mlr.press/v97/radanovic19a.html)] - On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. [[PUB](http://proceedings.mlr.press/v97/yu19d.html)] #### jmlr - Deep Reinforcement Learning for Swarm Systems. [[PUB](https://jmlr.org/papers/v20/18-476.html)] #### machine learning - Collaborative topic regression for predicting topic-based social influence. [[PUB](https://doi.org/10.1007/s10994-018-05776-w)] #### neurips - Communication trade-offs for Local-SGD with large step size. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/4aadd661908b181d059a117f02fbc9ec-Abstract.html)] - Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/80c0e8c4457441901351e4abbcf8c75c-Abstract.html)] - Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/4e87337f366f72daa424dae11df0538c-Abstract.html)] - Communication-efficient Distributed SGD with Sketching. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/75da5036f659fe64b53f3d9b39412967-Abstract.html)] - Double Quantization for Communication-Efficient Distributed Optimization. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/ea4eb49329550caaa1d2044105223721-Abstract.html)] - Learning to Optimize in Swarms. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/ec04e8ebba7e132043e5b4832e54f070-Abstract.html)] - Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/c17028c9b6e0c5deaad29665d582284a-Abstract.html)] - Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/d202ed5bcfa858c15a9f383c3e386ab2-Abstract.html)] - Robust and Communication-Efficient Collaborative Learning. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/3eb2f1a06667bfb9daba7f7effa0284b-Abstract.html)] ### 2018 #### icml - An Alternative View: When Does SGD Escape Local Minima?. [[PUB](http://proceedings.mlr.press/v80/kleinberg18a.html)] #### NeurIPS - cpSGD: Communication-efficient and differentially-private distributed SGD. [[PUB](https://papers.nips.cc/paper/2018/hash/21ce689121e39821d07d04faab328370-Abstract.html)] [[PDF](https://arxiv.org/abs/1805.10559)] - Collaborative Learning for Deep Neural Networks. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/430c3626b879b4005d41b8a46172e0c0-Abstract.html)] - Gradient Sparsification for Communication-Efficient Distributed Optimization. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/3328bdf9a4b9504b9398284244fe97c2-Abstract.html)] - Improved Algorithms for Collaborative PAC Learning. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/3569df159ec477451530c4455b2a9e86-Abstract.html)] - LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/feecee9f1643651799ede2740927317a-Abstract.html)] - Tight Bounds for Collaborative PAC Learning via Multiplicative Weights. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/ed519dacc89b2bead3f453b0b05a4a8b-Abstract.html)] #### uai - Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. [[PUB](http://auai.org/uai2018/proceedings/papers/354.pdf)] ### 2017 #### colt - Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. [[PUB](http://proceedings.mlr.press/v65/wang17a.html)] #### icml - Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. [[PUB](http://proceedings.mlr.press/v70/garber17a.html)] #### jmlr - CoCoA: A General Framework for Communication-Efficient Distributed Optimization. [[PUB](https://jmlr.org/papers/v18/16-512.html)] - Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. [[PUB](https://jmlr.org/papers/v18/15-397.html)] #### machine learning - Collaborative topic regression for online recommender systems: an online and Bayesian approach. [[PUB](https://doi.org/10.1007/s10994-016-5599-z)] #### NeurIPS - Federated Multi-Task Learning :fire:. [[PUB](https://papers.nips.cc/paper/2017/hash/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html)] [[PDF](https://arxiv.org/abs/1705.10467)] [[CODE](https://github.com/gingsmith/fmtl)] #### uai - Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. [[PUB](http://auai.org/uai2017/proceedings/papers/286.pdf)] ### 2016 #### jmlr - The Statistical Performance of Collaborative Inference. [[PUB](https://jmlr.org/papers/v17/15-346.html)] ### 2015 #### machine learning - Random drift particle swarm optimization algorithm: convergence analysis and parameter selection. [[PUB](https://doi.org/10.1007/s10994-015-5522-z)] ### 2014 #### icml - Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. [[PUB](http://proceedings.mlr.press/v32/shamir14.html)] #### machine learning - Collaborative filtering with information-rich and information-sparse entities. [[PUB](https://doi.org/10.1007/s10994-014-5454-z)] - Collaborative information acquisition for data-driven decisions. [[PUB](https://doi.org/10.1007/s10994-013-5424-x)] - Detecting inappropriate access to electronic health records using collaborative filtering. [[PUB](https://doi.org/10.1007/s10994-013-5376-1)] ### 2012 #### jmlr - SVDFeature: a toolkit for feature-based collaborative filtering. [[PUB](https://dl.acm.org/doi/10.5555/2503308.2503357)] ### 2011 #### colt - Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing. [[PUB](http://proceedings.mlr.press/v19/shamir11a/shamir11a.pdf)] #### machine learning - Editorial survey: swarm intelligence for data mining. [[PUB](https://doi.org/10.1007/s10994-010-5216-5)] - Particle swarm optimizer for variable weighting in clustering high-dimensional data. [[PUB](https://doi.org/10.1007/s10994-009-5154-2)] ### 2009 #### icml - Transfer learning for collaborative filtering via a rating-matrix generative model. [[PUB](https://doi.org/10.1145/1553374.1553454)] #### jmlr - A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. [[PUB](https://dl.acm.org/doi/10.5555/1577069.1577098)] - Particle Swarm Model Selection. [[PUB](https://dl.acm.org/doi/10.5555/1577069.1577084)] - Scalable Collaborative Filtering Approaches for Large Recommender Systems. [[PUB](https://dl.acm.org/doi/10.5555/1577069.1577091)] ### 2008 #### machine learning - A collaborative filtering framework based on both local user similarity and global user similarity. [[PUB](https://doi.org/10.1007/s10994-008-5068-4)] ### 2006 #### jmlr - Collaborative Multiagent Reinforcement Learning by Payoff Propagation. [[PUB](https://jmlr.org/papers/v7/kok06a.html)] ### 2005 #### colt - Competitive Collaborative Learning. [[PUB](https://doi.org/10.1007/11503415_16)] ### 2004 #### machine learning - Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. [[PUB](https://doi.org/10.1023/B:MACH.0000035516.74817.51)] #### uai - A Bayesian Approach toward Active Learning for Collaborative Filtering. [[PUB](https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&amp;smnu=2&amp;article_id=1119&amp;proceeding_id=20)] ### 2003 #### machine learning - A Theoretical Analysis of Query Selection for Collaborative Filtering. [[PUB](https://doi.org/10.1023/A:1022961719072)] #### uai - Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes. [[PUB](https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&amp;smnu=2&amp;article_id=981&amp;proceeding_id=19)] ### 2000 #### jmlr - Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. [[PUB](https://jmlr.org/papers/v1/heckerman00a.html)] #### tpami - Merging and Splitting Eigenspace Models. [[PUB](https://doi.org/10.1109/34.877525)]</details> ## fl in top dm conference and journal Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including [KDD](https://dblp.uni-trier.de/db/conf/kdd/index.html)(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and [WSDM](https://dblp.uni-trier.de/db/conf/wsdm/index.html)(Web Search and Data Mining). - [KDD](https://dblp.uni-trier.de/search?q=federate%20venue%3AKDD%3A) [2026](https://dl.acm.org/doi/proceedings/10.1145/3770854), [2025](https://dl.acm.org/doi/proceedings/10.1145/3690624), [2024](https://dl.acm.org/doi/proceedings/10.1145/3637528), [2023](https://dl.acm.org/doi/proceedings/10.1145/3580305)([Research Track](https://kdd.org/kdd2023/research-track-papers/), [Applied Data Science track](https://kdd.org/kdd2023/ads-track-papers/), [Workshop](https://fl4data-mining.github.io/papers/)), 2022([Research Track](https://kdd.org/kdd2022/paperRT.html), [Applied Data Science track](https://kdd.org/kdd2022/paperADS.html)), [2021](https://kdd.org/kdd2021/accepted-papers/index), [2020](https://www.kdd.org/kdd2020/accepted-papers) - [WSDM](https://dblp.uni-trier.de/search?q=federate%20venue%3AWSDM%3A) [2026](https://dl.acm.org/doi/proceedings/10.1145/3773966), [2025](https://www.wsdm-conference.org/2025/accepted-papers/), [2024](https://www.wsdm-conference.org/2024/accepted-papers/), [2023](https://www.wsdm-conference.org/2023/program/accepted-papers), [2022](https://www.wsdm-conference.org/2022/accepted-papers/), [2021](https://www.wsdm-conference.org/2021/accepted-papers.php), [2019](https://www.wsdm-conference.org/2019/accepted-papers.php) <details open> <summary>fl in top dm conference and journal</summary> <!-- START:fl-in-top-dm-conference-and-journal --> <!-- END:fl-in-top-dm-conference-and-journal --> ### 2026 #### KDD - Caesar: Optimizing Federated Learning via Low-deviation Compression. [[PUB](https://doi.org/10.1145/3770854.3780170)] - Communication-efficient Federated Graph Classification via Generative Diffusion Modeling. [[PUB](https://doi.org/10.1145/3770854.3780262)] - FedKDMR: Robust Federated Learning via Joint Knowledge Distillation &amp; Model Recombination. [[PUB](https://doi.org/10.1145/3770854.3780160)] - FedPRE: Robust Federated Graph Learning against Topological Corruption. [[PUB](https://doi.org/10.1145/3770854.3780330)] - HAL: Accurate, Private, and Efficient Sample Alignment for Multimodal Federated Learning. [[PUB](https://doi.org/10.1145/3770854.3780223)] - MFC: Mixed Federated Clustering based on Cross-modal Feature Decoupling. [[PUB](https://doi.org/10.1145/3770854.3780327)] - Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking. [[PUB](https://doi.org/10.1145/3770854.3780255)] - Two Heads Are Better Than One: Generalized Cross-Domain Federated Learning via Dual-Prototype. [[PUB](https://doi.org/10.1145/3770854.3780269)] - Vertical Federated K-Means for Multi-View Data Guided by a K-Means Cost Bound after Projection. [[PUB](https://doi.org/10.1145/3770854.3780182)] - MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation. [[PUB](https://doi.org/10.1145/3770854.3780264)] #### WSDM - Federated Watermarking of Deep Neural Networks with Distributed Verification. [[PUB](https://doi.org/10.1145/3773966.3777930)] - Sharpness-aware Federated Graph Learning. [[PUB](https://doi.org/10.1145/3773966.3777989)] ### 2025 #### KDD - A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3736825)] [[CODE](https://github.com/Jun-B0518/FedHydra)] - Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis. [[PUB](https://doi.org/10.1145/3690624.3709235)] - Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training. [[PUB](https://doi.org/10.1145/3690624.3709284)] - BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning. [[PUB](https://doi.org/10.1145/3690624.3709309)] [[CODE](https://github.com/ZhouYuCS/BTFL)] - DarkDistill: Difficulty-Aligned Federated Early-Exit Network Training on Heterogeneous Devices. [[PUB](https://doi.org/10.1145/3711896.3736902)] - FedAPM: Federated Learning via ADMM with Partial Model Personalization. [[PUB](https://doi.org/10.1145/3711896.3736954)] - FedDiAL: Adaptive Federated Learning with Hierarchical Discriminative Network for Large Pre-trained Models. [[PUB](https://doi.org/10.1145/3711896.3736955)] - Federated Continual Graph Learning. [[PUB](https://doi.org/10.1145/3711896.3736956)] - FedGuCci: Making Local Models More Connected in Landscape for Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3737037)] [[CODE](https://github.com/ZexiLee/fedgucci)] - FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [[PUB](https://doi.org/10.1145/3711896.3737861)] - FedMetro: Efficient Metro Passenger Flow Prediction via Federated Graph Learning. [[PUB](https://doi.org/10.1145/3711896.3737218)] [[CODE](https://github.com/AlexMufeng/FedMetro)] - FedSC: Federated Learning with Semantic-Aware Collaboration. [[PUB](https://doi.org/10.1145/3711896.3736957)] - FedVS: Towards Federated Vector Similarity Search with Filters. [[PUB](https://doi.org/10.1145/3711896.3736958)] - FEZE: Alignment-Flexible Zero-Shot Vertical Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3736959)] - FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning. [[PUB](https://doi.org/10.1145/3690624.3709346)] - Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning. [[PUB](https://doi.org/10.1145/3690624.3709311)] - Gradients as An Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing. [[PUB](https://doi.org/10.1145/3711896.3736987)] [[CODE](https://github.com/mastlab-T3S/FedRAS)] - GuardFGL: Similarity-driven Federated Graph Learning with Adversarial Robustness and Membership Privacy. [[PUB](https://doi.org/10.1145/3711896.3736994)] - HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark. [[PUB](https://doi.org/10.1145/3711896.3737379)] [[CODE](https://github.com/TsingZ0/HtFLlib)] - PARSIFAL: Private and Robust Sign Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3737074)] - PraFFL: A Preference-Aware Scheme in Fair Federated Learning. [[PUB](https://doi.org/10.1145/3690624.3709217)] [[CODE](https://github.com/rG223/PraFFL)] - Proxy-Validated Importance-Aware Federated Sample Selection with Meta Learning. [[PUB](https://doi.org/10.1145/3711896.3737093)] [[CODE](https://github.com/nameyzhang/FedSelect)] - Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness. [[PUB](https://doi.org/10.1145/3690624.3709243)] - Tackling Federated Long-Tailed Learning via Synthetic Feature-Based Decoupled Training. [[PUB](https://doi.org/10.1145/3711896.3737143)] - Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression. [[PUB](https://doi.org/10.1145/3690624.3709341)] [[CODE](https://github.com/JunliangLv/task_diversity_BFL)] - Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift. [[PUB](https://doi.org/10.1145/3711896.3737161)] - Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning. [[PUB](https://doi.org/10.1145/3711896.3737198)] - Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search. [[PUB](https://doi.org/10.1145/3690624.3709408)] #### WSDM - Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703513)] - FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703493)] - Towards Personalized Federated Multi-Scenario Multi-Task Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703523)] - Density-aware and Cluster-based Federated Anomaly Detection on Data Streams. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703548)] - Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3704125)] ### 2024 #### KDD - Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671722)] - *BadSampler:* Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671879)] - Federated Graph Learning with Structure Proxy Alignment. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671717)] [[CODE](https://github.com/xbfu/FedSpray)] - HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671660)] - FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671545)] - Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671590)] [[CODE](https://github.com/illidanlab/distributed-cluster-harmonization)] - FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671573)] [[CODE](https://github.com/alibaba/FederatedScope/tree/llm)] - On the Convergence of Zeroth-Order Federated Tuning for Large Language Models. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671865)] - CASA: Clustered Federated Learning with Asynchronous Clients. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671979)] - FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671990)] - Privacy-Preserving Federated Learning using Flower Framework. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671447)] - FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671748)] [[CODE](https://github.com/wangzihuixmu/FedSAC)] - FedNLR: Federated Learning with Neuron-wise Learning Rates. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3672042)] - FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671897)] - FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671899)] [[CODE](https://github.com/XTxiatong/FLea.git)] - Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671663)] - PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671753)] - FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671906)] - FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671613)] [[CODE](https://github.com/LarryHawkingYoung/KDD2024_FedGTP)] - OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671582)] [[CODE](https://github.com/rui-ye/OpenFedLLM)] - Personalized Federated Continual Learning via Multi-Granularity Prompt. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671948)] - Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671908)] [[CODE](https://github.com/ZhangJiayuan-BUAA/FedTSA)] - GPFedRec: Graph-Guided Personalization for Federated Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671702)] [[CODE](https://github.com/Zhangcx19/GPFedRec)] - Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671930)] - VertiMRF: Differentially Private Vertical Federated Data Synthesis. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671771)] - FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671490)] - Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations. [[PUB](https://doi.org/10.1145/3637528.3671743)] - High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. [[PUB](https://doi.org/10.1145/3637528.3672038)] [[CODE](https://github.com/FutureComputing4AI/ProxCSL)] - Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering. [[PUB](https://doi.org/10.1145/3637528.3671840)] [[CODE](https://github.com/wu1hong/SCCF)] #### WSDM - User Consented Federated Recommender System Against Personalized Attribute Inference Attack. [[PUB](https://dl.acm.org/doi/10.1145/3616855.3635830)] [[PDF](https://arxiv.org/abs/2312.16203)] [[CODE](https://github.com/hkust-knowcomp/uc-fedrec)] - Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3616855.3635758)] ### 2023 #### KDD - Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599475)] [[PDF](https://arxiv.org/abs/2210.11050)] - FedDefender: Client-Side Attack-Tolerant Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599346)] [[PDF](https://arxiv.org/abs/2307.09048)] [[CODE](https://github.com/deu30303/feddefender)] - FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599344)] [[CODE](https://github.com/zhenqincn/FedAPEN)] - FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599348)] [[PDF](https://arxiv.org/abs/2207.05247)] - ShapleyFL: Robust Federated Learning Based on Shapley Value. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599500)] [[CODE](https://github.com/ZJU-DIVER/ShapleyFL-Robust-Federated-Learning-Based-on-Shapley-Value)] - Federated Few-shot Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599347)] [[PDF](https://arxiv.org/abs/2306.10234)] [[CODE](https://github.com/songw-sw/f2l)] - Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599521)] - Personalized Federated Learning with Parameter Propagation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599464)] - Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599499)] [[PDF](https://arxiv.org/abs/2308.03035)] [[CODE](https://github.com/xidongwu/D-AUPRC)] - CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599293)] [[PDF](https://arxiv.org/abs/2109.05613)] - FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599354)] [[PDF](https://arxiv.org/abs/2306.03834)] - FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599345)] [[PDF](https://arxiv.org/abs/2307.01217)] [[CODE](https://github.com/tsingz0/fedcp)] - Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599443)] [[PDF](https://arxiv.org/abs/2301.00489)] [[CODE](https://github.com/jiayunz/fedalign)] - DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599311)] [[CODE](https://github.com/garyzhang99/DM-PFL)] - FS-REAL: Towards Real-World Cross-Device Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599829)] [[PDF](https://arxiv.org/abs/2303.13363)] - FedMultimodal: A Benchmark for Multimodal Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599825)] [[PDF](https://arxiv.org/abs/2306.09486)] [[CODE](https://github.com/usc-sail/fed-multimodal)] - PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599889)] [[PDF](https://arxiv.org/abs/2204.08146)] [[NEWS](http://info.ruc.edu.cn/xwgg/xyxw/e4c838332c3a46cd8b959be49c021bb1.htm)] - Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599898)] [[PDF](https://arxiv.org/abs/2302.01677)] [[CODE](https://github.com/alibaba/FederatedScope/tree/backdoor-bench)] - UA-FedRec: Untargeted Attack on Federated News Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599923)] [[PDF](https://arxiv.org/abs/2202.06701)] [[CODE](https://github.com/yjw1029/ua-fedrec)] - International Workshop on Federated Learning for Distributed Data Mining. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599198)] [[PAGE](https://fl4data-mining.github.io/)] - Is Normalization Indispensable for Multi-domain Federated Learning?. [[PUB](https://openreview.net/forum?id=ZiaOEg8XiGN)] - Distributed Personalized Empirical Risk Minimization. [[PUB](https://openreview.net/forum?id=k2eYX1p-Yb)] - Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=aJhe-VC0Ue)] - SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. [[PUB](https://openreview.net/forum?id=BVH3-XCRoN3)] - Optimization of User Resources in Federated Learning for Urban Sensing Applications. [[PUB](https://openreview.net/forum?id=D6ZQJ-szypI)] - FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. [[PUB](https://openreview.net/forum?id=nXjyCmLOYj)] - Federated Graph Analytics with Differential Privacy. [[PUB](https://openreview.net/forum?id=yBMbtNM3GR4)] - Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. [[PUB](https://openreview.net/forum?id=rAHB4qkWYz)] - Uncertainty Quantification in Federated Learning for Heterogeneous Health Data. [[PUB](https://openreview.net/forum?id=QSQOTUVQR46)] - A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. [[PUB](https://openreview.net/forum?id=pLEQFXACNA)] - Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. [[PUB](https://openreview.net/forum?id=_Nsxwk3WWew)] - Federated Blood Supply Chain Demand Forecasting: A Case Study. [[PUB](https://openreview.net/forum?id=2c0hdQDvf5g)] - Stochastic Clustered Federated Learning. [[PUB](https://openreview.net/forum?id=pFvTwedsUh)] - A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. [[PUB](https://openreview.net/forum?id=jg3XzuNbS-0)] - Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. [[PUB](https://openreview.net/forum?id=W7LqmnU4TYZ)] - FedNoisy: A Federated Noisy Label Learning Benchmark. [[PUB](https://openreview.net/forum?id=cXMenagKy-7)] - Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. [[PUB](https://openreview.net/forum?id=DZvNrRNas6z)] - Federated learning for competing risk analysis in healthcare. [[PUB](https://openreview.net/forum?id=-HYSYe7uXRT)] - Federated Threat Detection for Smart Home IoT rules. [[PUB](https://openreview.net/forum?id=SK_KfAh8MtF)] - A Collaborative Transfer Learning Framework for Cross-domain Recommendation. [[PUB](https://doi.org/10.1145/3580305.3599758)] - Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting. [[PUB](https://doi.org/10.1145/3580305.3599279)] - Communication Efficient Distributed Newton Method with Fast Convergence Rates. [[PUB](https://doi.org/10.1145/3580305.3599280)] #### WSDM - Federated Unlearning for On-Device Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3539597.3570463)] [[PDF](https://arxiv.org/abs/2210.10958)] - 4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling. [[PUB](https://doi.org/10.1145/3539597.3572703)] ### 2022 #### KDD - FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning :fire:. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539112)] [[PDF](https://arxiv.org/abs/2204.05562)] [[CODE](https://github.com/alibaba/FederatedScope)] - Collaboration Equilibrium in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539237)] [[PDF](https://arxiv.org/abs/2108.07926)] [[CODE](https://github.com/cuis15/learning-to-collaborate)] - Connected Low-Loss Subspace Learning for a Personalization in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539254)] [[PDF](https://arxiv.org/abs/2109.07628)] [[CODE](https://github.com/vaseline555/superfed)] - FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539384)] - Communication-Efficient Robust Federated Learning with Noisy Labels. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539328)] [[PDF](https://arxiv.org/abs/2206.05558)] - FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539231)] [[PDF](https://arxiv.org/abs/2207.09209)] [[CODE](https://github.com/zaixizhang/FLDetector)] - Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539402)] [[PDF](https://arxiv.org/abs/2105.08925)] [[CODE](https://github.com/Di-Chai/FedEval)] - FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539308)] [[PDF](https://arxiv.org/abs/2205.15896)] - Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539047)] [[PDF](https://hufudb.com/static/paper/2022/SIGKDD2022_Fed-LTD%20Towards%20Cross-Platform%20Ride%20Hailing%20via.pdf)] [[解读](https://zhuanlan.zhihu.com/p/544183874)] - Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539039)] [[PDF](https://arxiv.org/abs/2202.08036)] - No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539086)] [[PDF](https://arxiv.org/abs/2202.08036)] - FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539119)] [[PDF](https://arxiv.org/abs/2202.04975)] [[CODE](https://github.com/wuch15/FedAttack)] - A Practical Introduction to Federated Learning. [[PUB](https://doi.org/10.1145/3534678.3542631)] - Connecting Low-Loss Subspace for Personalized Federated Learning. [[PUB](https://doi.org/10.1145/3534678.3539254)] - FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning. [[PUB](https://doi.org/10.1145/3534678.3539112)] [[CODE](https://github.com/alibaba/FederatedScope)] - FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients. [[PUB](https://doi.org/10.1145/3534678.3539231)] - Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning. [[PUB](https://doi.org/10.1145/3534678.3539022)] #### WSDM - PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [[PUB](https://dl.acm.org/doi/10.1145/3488560.3498386)] [[PDF](https://arxiv.org/abs/2110.10926)] - Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning. [[PUB](https://doi.org/10.1145/3488560.3498381)] ### 2021 #### KDD - Fed2: Feature-Aligned Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467309)] [[PDF](https://arxiv.org/abs/2111.14248)] - FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467254)] [[CODE](https://github.com/lxcnju/FedRepo)] - Federated Adversarial Debiasing for Fair and Trasnferable Representations. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467281)] [[PAGE](https://jyhong.gitlab.io/publication/fade2021kdd/)] [[CODE](https://github.com/illidanlab/FADE)] [[SLIDE](https://jyhong.gitlab.io/publication/fade2021kdd/slides.pdf)] - Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. [[PUB](https://dl.acm.org/doi/pdf/10.1145/3447548.3467371)] [[CODE](https://github.com/mengcz13/KDD2021_CNFGNN)] [[解读](https://zhuanlan.zhihu.com/p/434839878)] - AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467169)] [[PDF](https://arxiv.org/abs/2109.12519)] - FLOP: Federated Learning on Medical Datasets using Partial Networks. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467185)] [[PDF](https://arxiv.org/abs/2102.05218.pdf)] [[CODE](https://github.com/jianyizhang123/FLOP)] - Federated Adversarial Debiasing for Fair and Transferable Representations. [[PUB](https://doi.org/10.1145/3447548.3467281)] [[CODE](https://github.com/illidanlab/FADE)] - Towards Fair Federated Learning. [[PUB](https://doi.org/10.1145/3447548.3470814)] - Device-Cloud Collaborative Learning for Recommendation. [[PUB](https://doi.org/10.1145/3447548.3467097)] #### WSDM - A Practical Federated Learning Framework for Small Number of Stakeholders. [[PUB](https://dl.acm.org/doi/10.1145/3437963.3441702)] [[CODE](https://github.com/MTC-ETH/Federated-Learning-source)] - Federated Deep Knowledge Tracing. [[PUB](https://dl.acm.org/doi/10.1145/3437963.3441747)] [[CODE](https://github.com/hxwujinze/federated-deep-knowledge-tracing)] ### 2020 #### KDD - FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. [[PUB](https://dl.acm.org/doi/10.1145/3394486.3403176)] [[VIDEO](https://papertalk.org/papertalks/23422)] - Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. [[PUB](https://dl.acm.org/doi/10.1145/3394486.3403298)] [[PDF](https://arxiv.org/abs/2008.06197)] [[VIDEO](https://papertalk.org/papertalks/23301)] ### 2019 #### kdd - A Collaborative Learning Framework to Tag Refinement for Points of Interest. [[PUB](https://doi.org/10.1145/3292500.3330698)] - FDML: A Collaborative Machine Learning Framework for Distributed Features. [[PUB](https://doi.org/10.1145/3292500.3330765)] #### WSDM - Federated Online Learning to Rank with Evolution Strategies. [[PUB](https://dl.acm.org/doi/10.1145/3289600.3290968)] [[CODE](http://github.com/facebookresearch/foltr-es)] ### 2018 #### kdd - Collaborative Deep Metric Learning for Video Understanding. [[PUB](https://doi.org/10.1145/3219819.3219856)] - Multi-label Learning with Highly Incomplete Data via Collaborative Embedding. [[PUB](https://doi.org/10.1145/3219819.3220038)] #### wsdm - Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation. [[PUB](https://doi.org/10.1145/3159652.3159675)] ### 2017 #### kdd - Federated Tensor Factorization for Computational Phenotyping. [[PUB](https://doi.org/10.1145/3097983.3098118)] - Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation. [[PUB](https://doi.org/10.1145/3097983.3098094)] - Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines. [[PUB](https://doi.org/10.1145/3097983.3098080)] #### wsdm - Representation Learning with Pair-wise Constraints for Collaborative Ranking. [[PUB](https://doi.org/10.1145/3018661.3018720)] ### 2016 #### kdd - Communication Efficient Distributed Kernel Principal Component Analysis. [[PUB](https://doi.org/10.1145/2939672.2939796)] ### 2015 #### kdd - Collaborative Deep Learning for Recommender Systems. [[PUB](https://doi.org/10.1145/2783258.2783273)] ### 2014 #### kdd - Active collaborative permutation learning. [[PUB](https://doi.org/10.1145/2623330.2623730)] ### 2012 #### kdd - Learning binary codes for collaborative filtering. [[PUB](https://doi.org/10.1145/2339530.2339611)] #### wsdm - Beyond ten blue links: enabling user click modeling in federated web search. [[PUB](https://doi.org/10.1145/2124295.2124351)] ### 2011 #### wsdm - On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. [[PUB](https://doi.org/10.1145/1935826.1935922)]</details> ## fl in top secure conference and journal Federated Learning papers accepted by top Secure conference and journal, Including [S&P](https://dblp.uni-trier.de/db/conf/sp/index.html)(IEEE Symposium on Security and Privacy), [CCS](https://dblp.uni-trier.de/db/conf/ccs/index.html)(Conference on Computer and Communications Security), [USENIX Security](https://dblp.uni-trier.de/db/conf/uss/index.html)(Usenix Security Symposium) and [NDSS](https://dblp.uni-trier.de/db/conf/ndss/index.html)(Network and Distributed System Security Symposium). - [S&P](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsp%3A) [2025](https://sp2025.ieee-security.org/program-papers.html), [2024](https://sp2024.ieee-security.org/program-papers.html), [2023](https://sp2023.ieee-security.org/program-papers.html), [2022](https://www.ieee-security.org/TC/SP2022/program-papers.html), [2019](https://www.ieee-security.org/TC/SP2019/program-papers.html) - [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) [2025](https://dl.acm.org/doi/proceedings/10.1145/3719027), [2024](https://dl.acm.org/doi/proceedings/10.1145/3658644), [2023](https://dl.acm.org/doi/proceedings/10.1145/3576915), [2022](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [2021](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [2019](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/), [2017](https://acmccs.github.io/papers/) - [USENIX Security](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fuss%3A) [2025](https://www.usenix.org/conference/usenixsecurity25/technical-sessions), [2024](https://www.usenix.org/conference/usenixsecurity24/technical-sessions), [2023](https://www.usenix.org/conference/usenixsecurity23/technical-sessions), [2022](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [2020](https://www.usenix.org/conference/usenixsecurity20/technical-sessions) - [NDSS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANDSS%3A) [2026](https://www.ndss-symposium.org/ndss2026/accepted-papers/), [2025](https://www.ndss-symposium.org/ndss2025/accepted-papers/), [2024](https://www.ndss-symposium.org/ndss2024/accepted-papers/), [2023](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [2022](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [2021](https://www.ndss-symposium.org/ndss2021/accepted-papers/) <details open> <summary>fl in top secure conference and journal</summary> <!-- START:fl-in-top-secure-conference-and-journal --> <!-- END:fl-in-top-secure-conference-and-journal --> ### 2026 #### NDSS - A Unified Defense Framework Against Membership Inference in Federated Learning via Distillation and Contribution-Aware Aggregation. [[PUB](https://www.ndss-symposium.org/ndss-paper/a-unified-defense-framework-against-membership-inference-in-federated-learning-via-distillation-and-contribution-aware-aggregation/)] - Entente: Cross-silo Intrusion Detection on Network Log Graphs with Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/entente-cross-silo-intrusion-detection-on-network-log-graphs-with-federated-learning/)] - ZKSL: Verifiable and Efficient Split Federated Learning via Asynchronous Zero-Knowledge Proofs. [[PUB](https://www.ndss-symposium.org/ndss-paper/zksl-verifiable-and-efficient-split-federated-learning-via-asynchronous-zero-knowledge-proofs/)] - SVDefense: Effective Defense against Gradient Inversion Attacks via Singular Value Decomposition. [[PUB](https://www.ndss-symposium.org/ndss-paper/svdefense-effective-defense-against-gradient-inversion-attacks-via-singular-value-decomposition/)] ### 2025 #### CCS - Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation. [[PUB](https://doi.org/10.1145/3719027.3765216)] - FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning. [[PUB](https://doi.org/10.1145/3719027.3744883)] - Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization. [[PUB](https://doi.org/10.1145/3719027.3765044)] - On Hyperparameters and Backdoor-Resistance in Horizontal Federated Learning. [[PUB](https://doi.org/10.1145/3719027.3765211)] - Poster: Adaptive Gradient Clipping with Personalized Differential Privacy for Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1145/3719027.3760710)] - Secure Noise Sampling for Differentially Private Collaborative Learning. [[PUB](https://doi.org/10.1145/3719027.3744834)] #### USENIX Security - DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/gu-xiaolan)] - FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/diaa)] - From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/xu-xiangrui)] - PoiSAFL: Scalable Poisoning Attack Framework to Byzantine-resilient Semi-asynchronous Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/pang-xiaoyi)] - Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/fan-refiner)] - SoK: Gradient Inversion Attacks in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/carletti)] - SoK: On Gradient Leakage in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/du)] - From Purity to Peril: Backdooring Merged Models From &quot;Harmless&quot; Benign Components. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/wang-lijin)] #### S&P - Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/11023255)] - Practical Poisoning Attacks with Limited Byzantine Clients in Clustered Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/11023464)] - An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models. [[PUB](https://ieeexplore.ieee.org/document/11050826)] - Privacy-Preserving Mutual Authentication Protocol for Federated Learning in Intelligent Transportation Systems. [[PUB](https://ieeexplore.ieee.org/document/11050805)] - FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/11050846)] - Enhancing Jailbreak Resistance in Large Language Models Using Model Merge. [[PUB](https://doi.org/10.1109/SPW67851.2025.00015)] - On the Conflict Between Robustness and Learning in Collaborative Machine Learning. [[PUB](https://doi.org/10.1109/SP61157.2025.00249)] #### NDSS - Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models. [[PUB](https://www.ndss-symposium.org/ndss-paper/privacy-preserving-data-deduplication-for-enhancing-federated-learning-of-language-models/)] - Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction. [[PUB](https://www.ndss-symposium.org/ndss-paper/scale-mia-a-scalable-model-inversion-attack-against-secure-federated-learning-via-latent-space-reconstruction/)] [[CODE](https://github.com/unknown123489/Scale-MIA)] - URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/urvfl-undetectable-data-reconstruction-attack-on-vertical-federated-learning/)] [[CODE](https://github.com/duanyiyao/URVFL)] - RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation. [[PUB](https://www.ndss-symposium.org/ndss-paper/raifle-reconstruction-attacks-on-interaction-based-federated-learning-with-adversarial-data-manipulation/)] [[CODE](https://github.com/dzungvpham/raifle)] - CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling. [[PUB](https://www.ndss-symposium.org/ndss-paper/censor-defense-against-gradient-inversion-via-orthogonal-subspace-bayesian-sampling/)] ### 2024 #### USENIX Security - ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/xu-zhangchen)] - BackdoorIndicator: Leveraging OOD Data for Proactive Backdoor Detection in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/li-songze)] - Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/tan)] - Efficient Privacy Auditing in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/chang)] - FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/cai-yifeng)] - Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/jiang-zhifeng)] - Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/lyu)] - Accelerating Secure Collaborative Machine Learning with Protocol-Aware RDMA. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/ren)] #### CCS - Byzantine-Robust Decentralized Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3670307)] - Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3670365)] - Cross-silo Federated Learning with Record-level Personalized Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3670351)] - Samplable Anonymous Aggregation for Private Federated Data Analysis. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690224)] - Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690200)] - Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690187)] [[CODE](https://github.com/Yuxin104/Opt-GDBA)] - Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690191)] - Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3691411)] - Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3691383)] - BadMerging: Backdoor Attacks Against Model Merging. [[PUB](https://doi.org/10.1145/3658644.3690284)] [[CODE](https://github.com/jzhang538/BadMerging)] - CoGNN: Towards Secure and Efficient Collaborative Graph Learning. [[PUB](https://doi.org/10.1145/3658644.3670300)] - Uncovering Gradient Inversion Risks in Practical Language Model Training. [[PUB](https://doi.org/10.1145/3658644.3690292)] #### NDSS - FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting. [[PUB](https://www.ndss-symposium.org/ndss-paper/fp-fed-privacy-preserving-federated-detection-of-browser-fingerprinting/)] - FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/freqfed-a-frequency-analysis-based-approach-for-mitigating-poisoning-attacks-in-federated-learning/)] - Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/automatic-adversarial-adaption-for-stealthy-poisoning-attacks-in-federated-learning/)] - CrowdGuard: Federated Backdoor Detection in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/crowdguard-federated-backdoor-detection-in-federated-learning/)] - Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption. [[PUB](https://www.ndss-symposium.org/ndss-paper/pencil-private-and-extensible-collaborative-learning-without-the-non-colluding-assumption/)] #### S&P - Protecting Label Distribution in Cross-Silo Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10646748)] - FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks. [[PUB](https://ieeexplore.ieee.org/document/10646613)] - BadVFL: Backdoor Attacks in Vertical Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10646664)] - SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks. [[PUB](https://ieeexplore.ieee.org/document/10646830)] - Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation. [[PUB](https://ieeexplore.ieee.org/document/10646724)] - LayerDBA: Circumventing Similarity-Based Defenses in Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10795458/)] - Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. [[PUB](https://ieeexplore.ieee.org/document/10579513/)] - A Performance Analysis for Confidential Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10579526)] ### 2023 #### CCS - Turning Privacy-preserving Mechanisms against Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623114)] [[PDF](https://arxiv.org/abs/2305.05355)] - MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623212)] - martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623134)] [[PDF](https://arxiv.org/abs/2309.01098)] [[CODE](https://github.com/liqi16/martfl)] - Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623193)] [[PDF](https://arxiv.org/abs/2209.04030)] - Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3624371)] - Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3624371)] - Turning Privacy-preserving Mechanisms against Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623114)] [[PDF](https://arxiv.org/abs/2305.05355)] [[CODE](https://github.com/DCALab-UNIPV/Turning-Privacy-preserving-Mechanisms-against-Federated-Learning)] #### USENIX Security - Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/mozaffari)] [[PDF](https://arxiv.org/abs/2110.04350)] - PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/yang-yuchen)] [[CODE](https://github.com/BHui97/PrivateFL)] - Gradient Obfuscation Gives a False Sense of Security in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/yue)] [[PDF](https://arxiv.org/abs/2206.04055)] [[CODE](https://github.com/KAI-YUE/rog)] - FedVal: Different good or different bad in federated learning. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/valadi)] [[PDF](https://arxiv.org/abs/2306.04040)] [[CODE](https://github.com/viktorvaladi/fedval)] - HOLMES: Efficient Distribution Testing for Secure Collaborative Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/chang)] #### NDSS - Securing Federated Sensitive Topic Classification against Poisoning Attacks. [[PUB](https://www.ndss-symposium.org/ndss-paper/securing-federated-sensitive-topic-classification-against-poisoning-attacks/)] [[PDF](https://arxiv.org/abs/2201.13086)] [[CODE](https://github.com/FRM-Sec/FRM)] - PPA: Preference Profiling Attack Against Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/ppa-preference-profiling-attack-against-federated-learning/)] [[PDF](https://arxiv.org/abs/2202.04856)] #### S&P - FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information. [[PUB](https://www.computer.org/csdl/proceedings-article/sp/2023/933600a326/1He7Y3q8FMY)] [[PDF](https://arxiv.org/abs/2210.10936)] - Scalable and Privacy-Preserving Federated Principal Component Analysis. [[PUB](https://ieeexplore.ieee.org/document/10179350)] [[PDF](https://arxiv.org/abs/2304.00129)] - BayBFed: Bayesian Backdoor Defense for Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179362)] [[PDF](https://arxiv.org/abs/2301.09508)] - 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179401)] [[CODE](https://github.com/haoyangliASTAPLE/3DFed)] - RoFL: Robustness of Secure Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179400)] [[PDF](https://arxiv.org/abs/2107.03311)] [[CODE](https://github.com/pps-lab/rofl-project-code)] - Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179434)] [[CODE](https://github.com/eniac/flamingo)] - ELSA: Secure Aggregation for Federated Learning with Malicious Actors. - Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. [[PUB](https://www.computer.org/csdl/proceedings-article/sp/2023/933600a076/1He7XMLcnsc)] [[PDF](https://arxiv.org/abs/2208.08662)] - SafeFL: MPC-friendly Framework for Private and Robust Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10188630)] - On the Pitfalls of Security Evaluation of Robust Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10188636)] - ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems. [[PUB](https://doi.org/10.1109/SP46215.2023.10179446)] - ELSA: Secure Aggregation for Federated Learning with Malicious Actors. [[PUB](https://doi.org/10.1109/SP46215.2023.10179468)] ### 2022 #### CCS - CERBERUS: Exploring Federated Prediction of Security Events. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560580)] [[PDF](https://arxiv.org/abs/2209.03050)] - EIFFeL: Ensuring Integrity for Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560611)] [[PDF](https://arxiv.org/abs/2112.12727)] - Eluding Secure Aggregation in Federated Learning via Model Inconsistency. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560557)] [[PDF](https://arxiv.org/abs/2111.07380)] [[CODE](https://github.com/pasquini-dario/eludingsecureaggregation)] - Federated Boosted Decision Trees with Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560687)] [[PDF](https://arxiv.org/abs/2210.02910)] [[CODE](https://github.com/Samuel-Maddock/federated-boosted-dp-trees)] #### S&P - Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/9833647/)] [[VIDEO](https://www.youtube.com/watch?v=tQv3CpxIyvs)] - SNARKBlock: Federated Anonymous Blocklisting from Hidden Common Input Aggregate Proofs. [[PUB](https://doi.org/10.1109/SP46214.2022.9833656)] #### USENIX Security - SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/chandran)] [[PDF](https://eprint.iacr.org/2021/1538)] [[CODE](https://github.com/shahakash28/simc)] [[VIDEO](https://www.youtube.com/watch?v=0Oaqi0JHUac)] [[SUPP](https://www.usenix.org/system/files/usenixsecurity22-chandran.pdf)] - Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/stevens)] [[SLIDE](https://www.usenix.org/system/files/sec22_slides-stevens.pdf)] [[VIDEO](https://www.youtube.com/watch?v=9kYHQkr6DuE)] - Label Inference Attacks Against Vertical Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/fu-chong)] [[SLIDE](https://www.usenix.org/system/files/sec22_slides-fu-chong.pdf)] [[CODE](https://github.com/FuChong-cyber/label-inference-attacks)] [[VIDEO](https://www.youtube.com/watch?v=JEmRbDtosVw)] - FLAME: Taming Backdoors in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/nguyen)] [[SLIDE](https://www.usenix.org/system/files/sec22_slides-nguyen.pdf)] [[PDF](https://arxiv.org/abs/2101.02281)] [[VIDEO](https://www.youtube.com/watch?v=nMrte2S9U68)] #### NDSS - Local and Central Differential Privacy for Robustness and Privacy in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-204/)] [[PDF](https://arxiv.org/abs/2009.03561)] [[VIDEO](https://www.youtube.com/watch?v=_aH2j5A3608&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=3)] [[UC.](https://github.com/wenzhu23333/Differential-Privacy-Based-Federated-Learning)] - Interpretable Federated Transformer Log Learning for Cloud Threat Forensics. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-236/)] [[VIDEO](https://www.youtube.com/watch?v=3HoysA6hsC8&list=PLfUWWM-POgQsS08uHJUJI6sawDO_3sNh0&index=3)] [[UC.](https://github.com/cyberthreat-datasets/ctdd-2021-os-syslogs)] - FedCRI: Federated Mobile Cyber-Risk Intelligence. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-229/)] [[VIDEO](https://www.youtube.com/watch?v=2zmdPqCCFxg&list=PLfUWWM-POgQs8ZZMMCX1RoNnmSQ70QXxd&index=3)] - DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-205/)] [[PDF](https://arxiv.org/abs/2201.00763)] [[VIDEO](https://www.youtube.com/watch?v=MJF_7vnoGh4&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=4)] ### 2021 #### CCS - Private Hierarchical Clustering in Federated Networks. [[PUB](https://dl.acm.org/doi/10.1145/3460120.3484822)] [[PDF](https://arxiv.org/abs/2105.09057)] #### NDSS - FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. [[PUB](https://www.ndss-symposium.org/ndss-paper/fltrust-byzantine-robust-federated-learning-via-trust-bootstrapping/)] [[PDF](https://arxiv.org/abs/2012.13995)] [[CODE](https://people.duke.edu/~zg70/code/fltrust.zip)] [[VIDEO](https://www.youtube.com/watch?v=zhhdPgKPCN0&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=2)] [[SLIDE](https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf)] - POSEIDON: Privacy-Preserving Federated Neural Network Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/poseidon-privacy-preserving-federated-neural-network-learning/)] [[VIDEO](https://www.youtube.com/watch?v=kX6-PMzxZ3c&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=1)] - Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/manipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning/)] [[CODE](https://github.com/vrt1shjwlkr/NDSS21-Model-Poisoning)] [[VIDEO](https://www.youtube.com/watch?v=G2VYRnLqAXE&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=3)] #### s&p - SAFELearn: Secure Aggregation for private FEderated Learning. [[PUB](https://doi.org/10.1109/SPW53761.2021.00017)] #### S&P Workshop - SAFELearn: Secure Aggregation for private FEderated Learning. [[PUB](https://ieeexplore.ieee.org/document/9474309)] #### usenix security - Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity21/presentation/zheng)] ### 2020 #### ndss - Strong Authentication without Temper-Resistant Hardware and Application to Federated Identities. [[PUB](https://www.ndss-symposium.org/ndss-paper/strong-authentication-without-temper-resistant-hardware-and-application-to-federated-identities/)] #### s&p - The Value of Collaboration in Convex Machine Learning with Differential Privacy. [[PUB](https://doi.org/10.1109/SP40000.2020.00025)] #### USENIX Security - Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity20/presentation/fang)] [[PDF](https://arxiv.org/abs/1911.11815)] [[CODE](https://people.duke.edu/~zg70/code/fltrust.zip)] [[VIDEO](https://www.youtube.com/watch?v=SQ12UpYrUVU&feature=emb_imp_woyt)] [[SLIDE](https://www.usenix.org/system/files/sec20_slides_fang.pdf)] ### 2019 #### CCS - A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [[PUB](https://dl.acm.org/doi/10.1145/3319535.3363256)] - Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [[PUB](https://doi.org/10.1145/3319535.3363256)] #### S&P - Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning :fire:. [[PUB](https://www.computer.org/csdl/proceedings-article/sp/2019/666000a739/1dlwhtj4r7O)] [[VIDEO](https://youtu.be/lzJY4BjCxTc)] [[SLIDE](https://www.ieee-security.org/TC/SP2019/SP19-Slides-pdfs/Milad_Nasr_-_08-Milad_Nasr-Comprehensive_Privacy_Analysis_of_Deep_Learning_)] [[CODE](https://github.com/privacytrustlab/ml_privacy_meter)] - IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/8844592)] - Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. [[PUB](https://doi.org/10.1109/SP.2019.00065)] - Exploiting Unintended Feature Leakage in Collaborative Learning. [[PUB](https://doi.org/10.1109/SP.2019.00029)] #### usenix security - Triton: A Software-Reconfigurable Federated Avionics Testbed. [[PUB](https://www.usenix.org/conference/cset19/presentation/crow)] ### 2018 #### ccs - The Price of Privacy in Collaborative Learning. [[PUB](https://doi.org/10.1145/3243734.3278525)] ### 2017 #### CCS - Practical Secure Aggregation for Privacy Preserving Machine Learning. [[PUB](https://dl.acm.org/doi/10.1145/3133956.3133982)] [[PDF](https://eprint.iacr.org/2017/281)] [[解读](https://zhuanlan.zhihu.com/p/445656765)] [[UC.](https://github.com/Chen-Junbao/SecureAggregation)] [[UC](https://github.com/corentingiraud/federated-learning-secure-aggregation)] - Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. [[PUB](https://doi.org/10.1145/3133956.3134012)] ### 2015 #### s&p - Privacy by Design in Federated Identity Management. [[PUB](https://doi.org/10.1109/SPW.2015.24)] ### 2014 #### ndss - Hardening Persona - Improving Federated Web Login. [[PUB](https://www.ndss-symposium.org/ndss2014/hardening-persona-improving-federated-web-login)]</details> ## fl in top cv conference and journal Federated Learning papers accepted by top CV(computer vision) conference and journal, Including [CVPR](https://dblp.uni-trier.de/db/conf/cvpr/index.html)(Computer Vision and Pattern Recognition), [ICCV](https://dblp.uni-trier.de/db/conf/iccv/index.html)(IEEE International Conference on Computer Vision), [ECCV](https://dblp.uni-trier.de/db/conf/eccv/index.html)(European Conference on Computer Vision), [MM](https://dblp.org/db/conf/mm/index.html)(ACM International Conference on Multimedia), [IJCV](https://dblp.uni-trier.de/db/journals/ijcv/index.html)(International Journal of Computer Vision). - [CVPR](https://dblp.uni-trier.de/search?q=federate%20venue%3ACVPR%3A) [2025](https://openaccess.thecvf.com/CVPR2025?day=all), [2024](https://openaccess.thecvf.com/CVPR2024?day=all), [2023](https://openaccess.thecvf.com/CVPR2023?day=all), [2022](https://openaccess.thecvf.com/CVPR2022), [2021](https://openaccess.thecvf.com/CVPR2021?day=all) - [ICCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AICCV%3A) [2023](https://openaccess.thecvf.com/ICCV2023?day=all), [2021](https://openaccess.thecvf.com/ICCV2021?day=all) - [ECCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AECCV%3A) [2024](https://www.ecva.net/papers.php), [2022](https://www.ecva.net/papers.php), [2020](https://www.ecva.net/papers.php) - [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) [2025](https://dl.acm.org/doi/proceedings/10.1145/3746027), [2024](https://dl.acm.org/doi/proceedings/10.1145/3664647), [2023](https://dl.acm.org/doi/proceedings/10.1145/3581783), [2022](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [2021](https://2021.acmmm.org/main-track-list), [2020](https://2020.acmmm.org/main-track-list.html) - [IJCV](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fijcv%3A) 2025, 2024 <details open> <summary>fl in top cv conference and journal</summary> <!-- START:fl-in-top-cv-conference-and-journal --> <!-- END:fl-in-top-cv-conference-and-journal --> ### 2026 #### ijcv - Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization. [[PUB](https://doi.org/10.1007/s11263-026-02777-4)] - CoSurfGS: 3D Surface Gaussian Splatting with Collaborative Distributed Learning for Large-scale Scene Reconstruction. [[PUB](https://doi.org/10.1007/s11263-025-02627-9)] ### 2025 #### iccv - A Framework for Double-Blind Federated Adaptation of Foundation Models. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00094)] [[CODE](https://github.com/tnurbek/blindfed)] - Class-Wise Federated Averaging for Efficient Personalization. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00173)] - Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00141)] [[CODE](https://github.com/LINs-lab/client2vec)] - Cooperative Pseudo Labeling for Unsupervised Federated Classification. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00318)] [[CODE](https://github.com/krumpguo/FedCoPL)] - EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00177)] - FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00237)] - FedAGC: Federated Continual Learning with Asymmetric Gradient Correction. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00366)] - FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00355)] [[CODE](https://github.com/hwang52/FedDifRC)] - Federated Continual Instruction Tuning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00131)] [[CODE](https://github.com/Ghy0501/FCIT)] - Federated Continuous Category Discovery and Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00234)] - Federated Domain Generalization with Domain-Specific Soft Prompts Generation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00228)] - Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00375)] - Federated Representation Angle Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00130)] - FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00209)] - FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01660)] [[CODE](https://github.com/mainaksingha01/FedMVP)] - FedPall: Prototype-Based Adversarial and Collaborative Learning for Federated Learning with Feature Drift. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00298)] [[CODE](https://github.com/DistriAI/FedPall)] - FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00649)] - FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00439)] - FedXDS: Leveraging Model Attribution Methods to Counteract Data Heterogeneity in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00435)] - Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00378)] - FLSeg: Enhancing Privacy and Robustness in Federated Learning under Heterogeneous Data via Model Segmentation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00373)] - Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00145)] [[CODE](https://github.com/zhentian777/FUCRT)] - Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00261)] - Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00020)] [[CODE](https://github.com/baowenxuan/Latte)] - LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00356)] - Neural Architecture Search Driven by Locally Guided Diffusion for Personalized Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00402)] - Personalized Federated Learning Under Local Supervision. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00388)] - Sibai: A Few-Shot Meta-Classifier for Poisoning Detection in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00361)] - Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00285)] - Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment. [[PUB](https://doi.org/10.1109/ICCV51701.2025.02708)] - Task-Aware Prompt Gradient Projection for Parameter-Efficient Tuning Federated Class-Incremental Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00253)] - Tensor-Aggregated LoRA in Federated Fine-Tuning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00106)] - Towards Privacy-preserved Pre-training of Remote Sensing Foundation Models with Federated Mutual-Guidance Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00176)] - You are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-Tailed Data. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00264)] [[CODE](https://github.com/shanss132/FedYoYo)] - COME: Dual Structure-Semantic Learning with Collaborative MOE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01993)] - Disrupting Model Merging: A Parameter-Level Defense without Sacrificing Accuracy. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01644)] [[CODE](https://github.com/ISCT-W/PaRaMS)] - Free-Merging: Fourier Transform for Efficient Model Merging. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00368)] [[CODE](https://github.com/Zhengsh123/FREE-Merging)] - FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00324)] - Task Vector Quantization for Memory-Efficient Model Merging. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01870)] - Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01176)] [[CODE](https://github.com/KongLingqi2333/WSL-VIReID)] #### MM - Client-Server Co-design with Multi-modal Codebooks Makes Better and Faster Federate Knowledge Sharing. [[PUB](https://doi.org/10.1145/3746027.3755311)] - Consistency of Local and Global Flatness for Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3755226)] [[CODE](https://github.com/junkangLiu0/FedNSAM)] - Discovering Maximum Frequency Consensus: Lightweight Federated Learning for Medical Image Segmentation. [[PUB](https://doi.org/10.1145/3746027.3755528)] - Diverse and Public Features Cooperation via Gradient Rectification for Federated Prompt Learning. [[PUB](https://doi.org/10.1145/3746027.3755308)] - DualFPT: Handling Data Heterogeneity in Federated Prompt Tuning from both Generalized and Personalized Perspective. [[PUB](https://doi.org/10.1145/3746027.3754872)] - DynFed: Adaptive Federated Learning via Quantization-Aware Knowledge Distillation. [[PUB](https://doi.org/10.1145/3746027.3755451)] - FeatShield: Isolating Malicious Feature Extractors for Backdoor-Robust Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3755742)] - FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models. [[PUB](https://doi.org/10.1145/3746027.3755387)] - FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3754814)] - FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3754587)] - Federated Incomplete Multi-view Clustering with Individual Structure Preservation and Central Representation Tensorization. [[PUB](https://doi.org/10.1145/3746027.3755799)] [[CODE](https://github.com/LiYannnnnudt/FIMC)] - FedRog: Robust Federated Graph Classification for Strong Heterogeneity and High-Noise Scenarios. [[PUB](https://doi.org/10.1145/3746027.3755358)] - FORGET ME: Federated Unlearning for Face Generation Models. [[PUB](https://doi.org/10.1145/3746027.3754935)] [[CODE](https://github.com/FanQi-AI/FFGU)] - Multi-Width Neural Network-Assisted Hierarchical Federated Learning in Heterogeneous Cloud-Edge-Device Computing. [[PUB](https://doi.org/10.1145/3746027.3754596)] - Positive Style Accumulation: A Style Screening and Continuous Utilization Framework for Federated DG-ReID. [[PUB](https://doi.org/10.1145/3746027.3755549)] - PriCAF: Privacy-Preserving Contribution Assessment in Federated Learning Before Model Training. [[PUB](https://doi.org/10.1145/3746027.3755825)] - Device-Cloud Collaborative Learning Framework for Efficient Unknown Object Detection. [[PUB](https://doi.org/10.1145/3746027.3755681)] - Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning. [[PUB](https://doi.org/10.1145/3746027.3755703)] - Multi-view Collaborative Representation Learning from Noisy Labels for VHR Imagery Classification. [[PUB](https://doi.org/10.1145/3746027.3755839)] - Outlier-Aware Model Merging for Efficient Multitask Inference. [[PUB](https://doi.org/10.1145/3746027.3754894)] - Spatial-Frequency Mamba Collaborative Learning Network for Infrared Small Target Detection. [[PUB](https://doi.org/10.1145/3746027.3754572)] - Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts. [[PUB](https://doi.org/10.1145/3746027.3755789)] - Tractography-Guided Dual-Label Collaborative Learning for Multi-Modal Cranial Nerves Parcellation. [[PUB](https://doi.org/10.1145/3746027.3755513)] #### CVPR - Federated Learning with Domain Shift Eraser. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Federated_Learning_with_Domain_Shift_Eraser_CVPR_2025_paper.html)] - Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Caldarola_Beyond_Local_Sharpness_Communication-Efficient_Global_Sharpness-aware_Minimization_for_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/pietrocagnasso/fedgloss)] - FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Chen_FedBiP_Heterogeneous_One-Shot_Federated_Learning_with_Personalized_Latent_Diffusion_Models_CVPR_2025_paper.html)] [[CODE](https://github.com/HaokunChen245/FedBiP)] - FedCS: Coreset Selection for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Hao_FedCS_Coreset_Selection_for_Federated_Learning_CVPR_2025_paper.html)] - AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/He_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.html)] [[CODE](https://github.com/ZHUANGHP/Analytic-federated-learning)] - NoT: Federated Unlearning via Weight Negation. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Khalil_NoT_Federated_Unlearning_via_Weight_Negation_CVPR_2025_paper.html)] - Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Kumar_Fortifying_Federated_Learning_Towards_Trustworthiness_via_Auditable_Data_Valuation_and_CVPR_2025_paper.html)] - Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Li_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.html)] - Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Mind_the_Gap_Confidence_Discrepancy_Can_Guide_Federated_Semi-Supervised_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/Jay-Codeman/SAGE)] - Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/WeiDai-David/2025CVPR_GGEUR)] - HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Raswa_HistoFS_Non-IID_Histopathologic_Whole_Slide_Image_Classification_via_Federated_Style_CVPR_2025_paper.html)] [[COCE](https://lalakitchen.github.io/HistoFS/)] - F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Saha_F3OCUS_-_Federated_Finetuning_of_Vision-Language_Foundation_Models_with_Optimal_CVPR_2025_paper.html)] [[PAGE](https://pramitsaha.github.io/FOCUS/)] - FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Shi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.html)] - FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Tan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html)] [[CODE](https://github.com/OakleyTan/FedSPA)] - Population Normalization for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Population_Normalization_for_Federated_Learning_CVPR_2025_paper.html)] - Model Poisoning Attacks to Federated Learning via Multi-Round Consistency. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Model_Poisoning_Attacks_to_Federated_Learning_via_Multi-Round_Consistency_CVPR_2025_paper.html)] [[CODE](https://github.com/xyq7/PoisonedFL/)] - dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xie_dFLMoE_Decentralized_Federated_Learning_via_Mixture_of_Experts_for_Medical_CVPR_2025_paper.html)] - Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.html)] [[CODE](https://github.com/JiiahaoXU/AlignIns)] - A Simple Data Augmentation for Feature Distribution Skewed Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yan_A_Simple_Data_Augmentation_for_Feature_Distribution_Skewed_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/IAMJackYan/FedRDN)] - Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.html)] - Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Subspace_Constraint_and_Contribution_Estimation_for_Heterogeneous_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/AVC2-UESTC/FedSCE.git)] - pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_pFedMxF_Personalized_Federated_Class-Incremental_Learning_with_Mixture_of_Frequency_Aggregation_CVPR_2025_paper.html)] - FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_FedCALM_Conflict-aware_Layer-wise_Mitigation_for_Selective_Aggregation_in_Deeper_Personalized_CVPR_2025_paper.html)] - Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Unlearning_through_Knowledge_Overwriting_Reversible_Federated_Unlearning_via_Selective_Sparse_CVPR_2025_paper.html)] - FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html)] [[CODE](https://github.com/Liar-Mask/FedMIA)] - Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Patient-Level_Anatomy_Meets_Scanning-Level_Physics_Personalized_Federated_Low-Dose_CT_Denoising_CVPR_2025_paper.html)] - FedMIA: An Effective Membership Inference Attack Exploiting &quot;All for One&quot; Principle in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html)] [[CODE](https://github.com/Liar-Mask/FedMIA)] - AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Du_AdaMMS_Model_Merging_for_Heterogeneous_Multimodal_Large_Language_Models_with_CVPR_2025_paper.html)] - Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Decouple-Then-Merge_Finetune_Diffusion_Models_as_Multi-Task_Learning_CVPR_2025_paper.html)] - Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Embracing_Collaboration_Over_Competition_Condensing_Multiple_Prompts_for_Visual_In-Context_CVPR_2025_paper.html)] [[CODE](https://github.com/gimpong/CVPR25-Condenser)] - Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Sami_Gradient_Inversion_Attacks_on_Parameter-Efficient_Fine-Tuning_CVPR_2025_paper.html)] [[CODE](https://github.com/info-ucr/PEFTLeak)] - How to Merge Your Multimodal Models Over Time?. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Dziadzio_How_to_Merge_Your_Multimodal_Models_Over_Time_CVPR_2025_paper.html)] - Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Learning_Dynamic_Collaborative_Network_for_Semi-supervised_3D_Vessel_Segmentation_CVPR_2025_paper.html)] [[CODE](https://github.com/xujiaommcome/DiCo)] - Less is More: Efficient Model Merging with Binary Task Switch. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Less_is_More_Efficient_Model_Merging_with_Binary_Task_Switch_CVPR_2025_paper.html)] - Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Libra-Merging_Importance-redundancy_and_Pruning-merging_Trade-off_for_Acceleration_Plug-in_in_Large_CVPR_2025_paper.html)] [[CODE](https://github.com/longrongyang/Libra-Merging)] - OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Tang_OnlineAnySeg_Online_Zero-Shot_3D_Segmentation_by_Visual_Foundation_Model_Guided_CVPR_2025_paper.html)] - PLeaS - Merging Models with Permutations and Least Squares. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Nasery_PLeaS_-_Merging_Models_with_Permutations_and_Least_Squares_CVPR_2025_paper.html)] - PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zou_PromptHashAffinity-Prompted_Collaborative_Cross-Modal_Learning_for_Adaptive_Hashing_Retrieval_CVPR_2025_paper.html)] [[CODE](https://github.com/ShiShuMo/PromptHash)] - Task Singular Vectors: Reducing Task Interference in Model Merging. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Gargiulo_Task_Singular_Vectors_Reducing_Task_Interference_in_Model_Merging_CVPR_2025_paper.html)] - Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Visual_and_Semantic_Prompt_Collaboration_for_Generalized_Zero-Shot_Learning_CVPR_2025_paper.html)] - Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Weakly_Supervised_Temporal_Action_Localization_via_Dual-Prior_Collaborative_Learning_Guided_CVPR_2025_paper.html)] #### IJCV - Relation-Guided Versatile Regularization for Federated Semi-Supervised Learning. [[PUB](https://link.springer.com/article/10.1007/s11263-024-02330-1)] - Achieving Procedure-Aware Instructional Video Correlation Learning Under Weak Supervision from a Collaborative Perspective. [[PUB](https://doi.org/10.1007/s11263-024-02272-8)] - HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning. [[PUB](https://doi.org/10.1007/s11263-024-02318-x)] [[CODE](https://github.com/ZengxiZhang/HUPE)] - Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID. [[PUB](https://doi.org/10.1007/s11263-025-02461-z)] [[CODE](https://github.com/FranklinLingfeng/code-for-SALCR)] ### 2024 #### MM - DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations. [[PUB](https://doi.org/10.1145/3664647.3681260)] [[CODE](https://github.com/GuogangZhu/DualFed)] - One-shot-but-not-degraded Federated Learning. [[PUB](https://doi.org/10.1145/3664647.3680715)] [[CODE](https://github.com/zenghui9977/IntactOFL)] - Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning. [[PUB](https://doi.org/10.1145/3664647.3681384)] [[CODE](https://github.com/SkyOfBeginning/FedCBC)] - FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models. [[PUB](https://doi.org/10.1145/3664647.3681490)] - Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition. [[PUB](https://doi.org/10.1145/3664647.3681588)] [[CODE](https://github.com/XinghaoWu/FedDecomp)] - CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation. [[PUB](https://doi.org/10.1145/3664647.3680867)] - Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training. [[PUB](https://doi.org/10.1145/3664647.3680733)] - FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework. [[PUB](https://doi.org/10.1145/3664647.3681545)] - One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity. [[PUB](https://doi.org/10.1145/3664647.3681054)] [[CODE](https://github.com/NaiboWang/FedELMY)] - FedSLS: Exploring Federated Aggregation in Saliency Latent Space. [[PUB](https://doi.org/10.1145/3664647.3681278)] - Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia. [[PUB](https://doi.org/10.1145/3664647.3680788)] - FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3664647.3681094)] - Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data. [[PUB](https://dl.acm.org/doi/10.1145/3664647.3681480)] - Cross-Modal Meta Consensus for Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1145/3664647.3681510)] - Masked Random Noise for Communication-Efficient Federated Learning. [[PUB](https://doi.org/10.1145/3664647.3680608)] - Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations. [[PUB](https://doi.org/10.1145/3664647.3681302)] - Adaptive Hierarchical Aggregation for Federated Object Detection. [[PUB](https://doi.org/10.1145/3664647.3681158)] - FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement. [[PUB](https://doi.org/10.1145/3664647.3681319)] - Federated Fuzzy C-means with Schatten-p Norm Minimization. [[PUB](https://doi.org/10.1145/3664647.3681557)] - Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation. [[PUB](https://doi.org/10.1145/3664647.3681415)] - CoPL: Parameter-Efficient Collaborative Prompt Learning for Audio-Visual Tasks. [[PUB](https://doi.org/10.1145/3664647.3681492)] #### IJCV - Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification. [[PUB](https://link.springer.com/article/10.1007/s11263-024-02077-9)] [[CODE](https://github.com/Zi-YuanYang/PSFed-Palm)] #### ECCV - SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72655-2_17)] [[CODE](https://github.com/KoalaYan/SkyMask)] - FedHide: Federated Learning by Hiding in the Neighbors. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72897-6_23)] - FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73668-1_14)] - FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73195-2_20)] - Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73030-6_10)] - Federated Learning with Local Openset Noisy Labels. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72754-2_3)] - FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73010-8_22)] - Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73004-7_11)] - BAFFLE: A Baseline of Backpropagation-Free Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73226-3_6)] [[CODE](https://github.com/FengHZ/BAFFLE)] - PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73650-6_9)] [[CODE](https://github.com/Ghy0501/PILoRA)] - Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72633-0_14)] - Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73229-4_2)] - FedHARM: Harmonizing Model Architectural Diversity in Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73036-8_3)] - SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72986-7_10)] - Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72952-2_8)] - Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73404-5_18)] - Towards Multi-modal Transformers in Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72633-0_13)] - Local and Global Flatness for Federated Domain Generalization. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73010-8_5)] - Feature Diversification and Adaptation for Federated Domain Generalization. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73220-1_4)] - PFEDEDIT: Personalized Federated Learning via Automated Model Editing. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72986-7_6)] - CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing. [[PUB](https://doi.org/10.1007/978-3-031-73247-8_1)] - Diffusion Soup: Model Merging for Text-to-Image Diffusion Models. [[PUB](https://doi.org/10.1007/978-3-031-73036-8_15)] - MAGMAX: Leveraging Model Merging for Seamless Continual Learning. [[PUB](https://doi.org/10.1007/978-3-031-73013-9_22)] - Model Breadcrumbs: Scaling Multi-task Model Merging with Sparse Masks. [[PUB](https://doi.org/10.1007/978-3-031-73226-3_16)] - Multi-branch Collaborative Learning Network for 3D Visual Grounding. [[PUB](https://doi.org/10.1007/978-3-031-72952-2_22)] - Training-Free Model Merging for Multi-target Domain Adaptation. [[PUB](https://doi.org/10.1007/978-3-031-72970-6_24)] #### CVPR - FedHCA2: Towards Hetero-Client Federated Multi-Task Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lu_FedHCA2_Towards_Hetero-Client_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2311.13250)] [[CODE](https://github.com/innovator-zero/FedHCA2)] - Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Fair_Federated_Learning_under_Domain_Skew_with_Local_Consistency_and_CVPR_2024_paper.html)] [[PDF](http://arxiv.org/abs/2405.16585)] [[CODE](https://github.com/yuhangchen0/FedHEAL)] - Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Think_Twice_Before_Selection_Federated_Evidential_Active_Learning_for_Medical_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Think_Twice_Before_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2312.02567)] [[CODE](https://github.com/JiayiChen815/FEAL)] - FedMef: Towards Memory-efficient Federated Dynamic Pruning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Huang_FedMef_Towards_Memory-efficient_Federated_Dynamic_Pruning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_FedMef_Towards_Memory-efficient_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.14737)] - Communication-Efficient Federated Learning with Accelerated Client Gradient. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Communication-Efficient_Federated_Learning_with_Accelerated_Client_Gradient_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Communication-Efficient_Federated_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2201.03172)] [[CODE](https://github.com/geehokim/FedACG)] - Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_Revamping_Federated_Learning_Security_from_a_Defenders_Perspective_A_Unified_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kumar_Revamping_Federated_Learning_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/NaveenKumar-1311/FCD)] - Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Qi_Adaptive_Hyper-graph_Aggregation_for_Modality-Agnostic_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qi_Adaptive_Hyper-graph_Aggregation_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/MM-Fed/HAMFL)] - Towards Efficient Replay in Federated Incremental Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Towards_Efficient_Replay_in_Federated_Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Towards_Efficient_Replay_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.05890)] - Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Mixed-Precision_Quantization_for_Federated_Learning_on_Resource-Constrained_Heterogeneous_Devices_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Mixed-Precision_Quantization_for_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2311.18129)] - Data Valuation and Detections in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Data_Valuation_and_Detections_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Data_Valuation_and_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2311.05304)] [[CODE](https://github.com/muz1lee/motdata)] - Decentralized Directed Collaboration for Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Decentralized_Directed_Collaboration_for_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Decentralized_Directed_Collaboration_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.17876)] - Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Deng_Unlocking_the_Potential_of_Prompt-Tuning_in_Bridging_Generalized_and_Personalized_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_Unlocking_the_Potential_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2310.18285)] [[CODE](https://github.com/ubc-tea/SGPT)] - Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Global_and_Local_Prompts_Cooperation_via_Optimal_Transport_for_Federated_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Global_and_Local_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.00041)] [[CODE](https://github.com/hongxialee/fedotp)] - Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Liao_Rethinking_the_Representation_in_Federated_Unsupervised_Learning_with_Non-IID_Data_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liao_Rethinking_the_Representation_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.16398)] [[CODE](https://github.com/XeniaLLL/FedU2)] - Relaxed Contrastive Learning for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Seo_Relaxed_Contrastive_Learning_for_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Seo_Relaxed_Contrastive_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2401.04928)] [[CODE](https://github.com/skynbe/FedRCL)] - Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Leak_and_Learn_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2403.18144)] [[VIDEO](https://www.youtube.com/watch?v=ovmSnjSOcks)] - Traceable Federated Continual Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Traceable_Federated_Continual_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/POwerWeirdo/TagFCL)] - Federated Online Adaptation for Deep Stereo. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Poggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Poggi_Federated_Online_Adaptation_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.14873)] [[CODE](https://github.com/mattpoggi/fedstereo)] [[PAGE](https://fedstereo.github.io/)] [[VIDEO](https://youtu.be/gVpWsjrUTJc)] - Federated Generalized Category Discovery. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Pu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Pu_Federated_Generalized_Category_CVPR_2024_supplemental.zip)] [[PDF](https://arxiv.org/abs/2305.14107)] [[CODE](https://github.com/TPCD/FedGCD)] - Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Le_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Le_Efficiently_Assemble_Normalization_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2403.15605)] [[CODE](https://github.com/lhkhiem28/gPerXAN)] - Text-Enhanced Data-free Approach for Federated Class-Incremental Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Tran_Text-Enhanced_Data-free_Approach_for_Federated_Class-Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tran_Text-Enhanced_Data-free_Approach_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.14101)] [[CODE](https://github.com/tmtuan1307/lander)] - PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Xie_PerAda_Parameter-Efficient_Federated_Learning_Personalization_with_Generalization_Guarantees_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_PerAda_Parameter-Efficient_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2302.06637)] [[CODE](https://github.com/NVlabs/PerAda)] - FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Lee_FedSOL_Stabilized_Orthogonal_Learning_with_Proximal_Restrictions_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_FedSOL_Stabilized_Orthogonal_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2308.12532)] [[CODE](https://github.com/Lee-Gihun/FedSOL)] - FedUV: Uniformity and Variance for Heterogeneous Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Son_FedUV_Uniformity_and_Variance_for_Heterogeneous_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Son_FedUV_Uniformity_and_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2402.18372)] - FedAS: Bridging Inconsistency in Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Yang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[CODE](https://github.com/xiyuanyang45/FedAS)] - FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Tamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tamirisa_FedSelect_Personalized_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2404.02478)] [[CODE](https://github.com/lapisrocks/fedselect)] - Device-Wise Federated Network Pruning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gao_Device-Wise_Federated_Network_CVPR_2024_supplemental.pdf)] - Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Byzantine-robust_Decentralized_Federated_Learning_via_Dual-domain_Clustering_and_Trust_Bootstrapping_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Byzantine-robust_Decentralized_Federated_CVPR_2024_supplemental.pdf)] - DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Bai_DiPrompT_Disentangled_Prompt_Tuning_for_Multiple_Latent_Domain_Generalization_in_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bai_DiPrompT_Disentangled_Prompt_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2403.08506)] - An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_An_Upload-Efficient_Scheme_for_Transferring_Knowledge_From_a_Server-Side_Pre-trained_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_An_Upload-Efficient_Scheme_CVPR_2024_supplemental.zip)] [[PDF](https://arxiv.org/abs/2403.15760)] [[CODE](https://github.com/tsingz0/fedktl)] [[POSTER](https://github.com/TsingZ0/FedKTL/blob/main/FedKTL.png)] [[SLIDES](https://github.com/TsingZ0/FedKTL/blob/main/FedKTL.pdf)] - An Aggregation-Free Federated Learning for Tackling Data Heterogeneity. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Wang_An_Aggregation-Free_Federated_Learning_for_Tackling_Data_Heterogeneity_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_An_Aggregation-Free_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2404.18962)] - FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_FLHetBench_Benchmarking_Device_and_State_Heterogeneity_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_FLHetBench_Benchmarking_Device_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/Carkham/FLHetBench)] [[PAGE](https://carkham.github.io/FL_Het_Bench/)] [[POSTER](https://drive.google.com/file/d/1Ln0cnptSn5EfML6ughQ7NowwjjLfMYgu/view?usp=sharing)] [[VIDEO](https://www.youtube.com/watch?v=zDGPt3929l8)] - Leak and Learn: An Attacker&apos;s Cookbook to Train Using Leaked Data from Federated Learning. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01164)] - Revamping Federated Learning Security from a Defender&apos;s Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02302)] - CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01028)] [[CODE](https://github.com/Nicholas0228/Revelio)] - Cloud-Device Collaborative Learning for Multimodal Large Language Models. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01202)] - DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02598)] - Dual-Enhanced Coreset Selection with Class-Wise Collaboration for Online Blurry Class Incremental Learning. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02265)] - Improving Plasticity in Online Continual Learning via Collaborative Learning. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02214)] [[CODE](https://github.com/maorong-wang/CCL-DC)] - NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging. [[PUB](https://doi.org/10.1109/CVPR52733.2024.00852)] [[CODE](https://github.com/univ-esuty/noisecollage)] - Shallow-Deep Collaborative Learning for Unsupervised Visible-Infrared Person Re-Identification. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01596)] - Training-Free Pretrained Model Merging. [[PUB](https://doi.org/10.1109/CVPR52733.2024.00565)] [[CODE](https://github.com/zju-vipa/training_free_model_merging)] - Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01180)] #### CVPR workshop - Collaborative Visual Place Recognition through Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Dutto_Collaborative_Visual_Place_Recognition_through_Federated_Learning_CVPRW_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/supplemental/Dutto_Collaborative_Visual_Place_CVPRW_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2404.13324)] - FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Gao_FedProK_Trustworthy_Federated_Class-Incremental_Learning_via_Prototypical_Feature_Knowledge_Transfer_CVPRW_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/supplemental/Gao_FedProK_Trustworthy_Federated_CVPRW_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.02685)] - Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Nakka_Federated_Hyperparameter_Optimization_Through_Reward-Based_Strategies_Challenges_and_Insights_CVPRW_2024_paper.html)] - On the Efficiency of Privacy Attacks in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Tabassum_On_the_Efficiency_of_Privacy_Attacks_in_Federated_Learning_CVPRW_2024_paper.html)] [[PDF](http://arxiv.org/abs/2404.09430)] ### 2023 #### ijcv - AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis. [[PUB](https://doi.org/10.1007/s11263-023-01791-0)] #### MM - FedCE: Personalized Federated Learning Method based on Clustering Ensembles. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612217)] - FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611958)] - Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612597)] [[PDF](https://arxiv.org/abs/2312.02416)] [[CODE](https://github.com/J1nqianChen/FedKA)] - Federated Deep Multi-View Clustering with Global Self-Supervision. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612027)] [[PDF](https://arxiv.org/abs/2309.13697)] - FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611953)] - Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3613837)] [[CODE](https://github.com/exquisite1210/PT-FUCH_P)] - Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612178)] [[PDF](https://arxiv.org/abs/2308.11646)] - FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611966)] - Federated Learning with Label-Masking Distillation. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611984)] [[CODE](https://github.com/wnma3mz/FedLMD)] - Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612481)] [[PDF](https://arxiv.org/abs/2308.03457)] [[CODE](https://github.com/qizhuang-qz/FedCSPC)] - A Four-Pronged Defense Against Byzantine Attacks in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612474)] [[PDF](https://arxiv.org/abs/2308.03331)] - Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611757)] - FedGH: Heterogeneous Federated Learning with Generalized Global Header. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611781)] [[PDF](https://arxiv.org/abs/2303.13137)] [[CODE](https://github.com/LipingYi/FedGH)] - Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612134)] [[PDF](https://arxiv.org/abs/2308.03432)] [[CODE](https://github.com/yuxuanzhang0713/fedcsr)] - AffectFAL: Federated Active Affective Computing with Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612442)] [[CODE](https://github.com/AffectFAL/AffectFAL)] - Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612350)] - Collaborative Learning of Diverse Experts for Source-free Universal Domain Adaptation. [[PUB](https://doi.org/10.1145/3581783.3612211)] - Gradient-Free Textual Inversion. [[PUB](https://doi.org/10.1145/3581783.3612599)] - Practical Edge Detection via Robust Collaborative Learning. [[PUB](https://doi.org/10.1145/3581783.3612099)] - Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement. [[PUB](https://doi.org/10.1145/3581783.3612077)] #### ICCV - Towards Attack-tolerant Federated Learning via Critical Parameter Analysis. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Han_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.09318)] [[CODE](https://github.com/Sungwon-Han/FEDCPA)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Han_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf)] - Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Efficient_Model_Personalization_in_Federated_Learning_via_Client-Specific_Prompt_Generation_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2308.15367)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Yang_Efficient_Model_Personalization_ICCV_2023_supplemental.pdf)] - Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper.html)] [[CODE](https://github.com/czhang024/CI-Net)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhang_Generative_Gradient_Inversion_ICCV_2023_supplemental.pdf)] - GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_GPFL_Simultaneously_Learning_Global_and_Personalized_Feature_Information_for_Personalized_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.10279)] [[CODE](https://github.com/TsingZ0/GPFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhang_GPFL_Simultaneously_Learning_ICCV_2023_supplemental.zip)] - Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_Workie-Talkie_Accelerating_Federated_Learning_by_Overlapping_Computing_and_Communications_via_ICCV_2023_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Chen_Workie-Talkie_Accelerating_Federated_ICCV_2023_supplemental.pdf)] - PGFed: Personalize Each Client's Global Objective for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Luo_PGFed_Personalize_Each_Clients_Global_Objective_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2212.01448)] [[CODE](https://github.com/ljaiverson/pgfed)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Luo_PGFed_Personalize_Each_ICCV_2023_supplemental.pdf)] - FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Sun_FedPerfix_Towards_Partial_Model_Personalization_of_Vision_Transformers_in_Federated_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2308.09160)] [[CODE](https://github.com/imguangyu/fedperfix)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Sun_FedPerfix_Towards_Partial_ICCV_2023_supplemental.pdf)] - L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Rehman_L-DAWA_Layer-wise_Divergence_Aware_Weight_Aggregation_in_Federated_Self-Supervised_Visual_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2307.07393)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Rehman_L-DAWA_Layer-wise_Divergence_ICCV_2023_supplemental.pdf)] - FedPD: Federated Open Set Recognition with Parameter Disentanglement. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_FedPD_Federated_Open_Set_Recognition_with_Parameter_Disentanglement_ICCV_2023_paper.html)] [[CODE](https://github.com/CityU-AIM-Group/FedPD)] - TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_TARGET_Federated_Class-Continual_Learning_via_Exemplar-Free_Distillation_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.06937)] [[CODE](https://github.com/zj-jayzhang/Federated-Class-Continual-Learning)] - Towards Instance-adaptive Inference for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Feng_Towards_Instance-adaptive_Inference_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.06051)] [[CODE](https://github.com/chunmeifeng/fedins)] - Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhou_Communication-efficient_Federated_Learning_with_Single-Step_Synthetic_Features_Compressor_for_Faster_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2302.13562)] [[CODE](https://github.com/Soptq/iccv23-3sfc)] - zPROBE: Zero Peek Robustness Checks for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Ghodsi_zPROBE_Zero_Peek_Robustness_Checks_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2206.12100)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Ghodsi_zPROBE_Zero_Peek_ICCV_2023_supplemental.pdf)] - ProtoFL: Unsupervised Federated Learning via Prototypical Distillation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Kim_ProtoFL_Unsupervised_Federated_Learning_via_Prototypical_Distillation_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2307.12450)] - MAS: Towards Resource-Efficient Federated Multiple-Task Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhuang_MAS_Towards_Resource-Efficient_Federated_Multiple-Task_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2307.11285)] [[CODE](https://github.com/EasyFL-AI/EasyFL/tree/master/applications/mas)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhuang_MAS_Towards_Resource-Efficient_ICCV_2023_supplemental.pdf)] - FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Guo_FSAR_Federated_Skeleton-based_Action_Recognition_with_Adaptive_Topology_Structure_and_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2306.11046)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Guo_FSAR_Federated_Skeleton-based_ICCV_2023_supplemental.pdf)] - When Do Curricula Work in Federated Learning?. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Vahidian_When_Do_Curricula_Work_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2212.12712)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Vahidian_When_Do_Curricula_ICCV_2023_supplemental.pdf)] - Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Sun_Communication-Efficient_Vertical_Federated_Learning_with_Limited_Overlapping_Samples_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.16270)] [[CODE](https://github.com/NVIDIA/NVFlare/tree/main/research/one-shot-vfl)] - Multi-Metrics Adaptively Identifies Backdoors in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Huang_Multi-Metrics_Adaptively_Identifies_Backdoors_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.06601)] [[CODE](https://github.com/siquanhuang/Multi-metrics_against_backdoors_in_FL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Huang_Multi-Metrics_Adaptively_Identifies_ICCV_2023_supplemental.pdf)] - No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Li_No_Fear_of_Classifier_Biases_Neural_Collapse_Inspired_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.10058)] [[CODE](https://github.com/zexilee/iccv-2023-fedetf)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Li_No_Fear_of_ICCV_2023_supplemental.pdf)] - FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_FRAug_Tackling_Federated_Learning_with_Non-IID_Features_via_Representation_Augmentation_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2205.14900)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Chen_FRAug_Tackling_Federated_ICCV_2023_supplemental.pdf)] - Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Wu_Bold_but_Cautious_Unlocking_the_Potential_of_Personalized_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2309.11103)] [[CODE](https://github.com/kxzxvbk/Fling)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Wu_Bold_but_Cautious_ICCV_2023_supplemental.pdf)] - Global Balanced Experts for Federated Long-Tailed Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zeng_Global_Balanced_Experts_for_Federated_Long-Tailed_Learning_ICCV_2023_paper.html)] [[CODE](https://github.com/Spinozaaa/Federated-Long-tailed-Learning)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zeng_Global_Balanced_Experts_ICCV_2023_supplemental.pdf)] - Knowledge-Aware Federated Active Learning with Non-IID Data. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Cao_Knowledge-Aware_Federated_Active_Learning_with_Non-IID_Data_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2211.13579)] [[CODE](https://github.com/ycao5602/KAFAL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Cao_Knowledge-Aware_Federated_Active_ICCV_2023_supplemental.pdf)] - Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Wan_Enhancing_Privacy_Preservation_in_Federated_Learning_via_Learning_Rate_Perturbation_ICCV_2023_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Wan_Enhancing_Privacy_Preservation_ICCV_2023_supplemental.pdf)] - Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Cho_Local_or_Global_Selective_Knowledge_Assimilation_for_Federated_Learning_with_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2307.08809)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Cho_Local_or_Global_ICCV_2023_supplemental.pdf)] - Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Hu_Federated_Learning_Over_Images_Vertical_Decompositions_and_Pre-Trained_Backbones_Are_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2309.03237)] [[CODE](https://github.com/huerdong/FedVert-Experiments)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Hu_Federated_Learning_Over_ICCV_2023_supplemental.pdf)] - Robust Heterogeneous Federated Learning under Data Corruption. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Fang_Robust_Heterogeneous_Federated_Learning_under_Data_Corruption_ICCV_2023_paper.html)] [[CODE](https://github.com/FangXiuwen/AugHFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Fang_Robust_Heterogeneous_Federated_ICCV_2023_supplemental.pdf)] - Personalized Semantics Excitation for Federated Image Classification. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Xia_Personalized_Semantics_Excitation_for_Federated_Image_Classification_ICCV_2023_paper.html)] [[CODE](https://github.com/HaifengXia/PSE)] - Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Do_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2207.09657)] [[CODE](https://github.com/aioz-ai/MultigraphFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Do_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_supplemental.pdf)] - Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00362)] - FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00111)] - FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00371)] - PGFed: Personalize Each Client&apos;s Global Objective for Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51070.2023.00365)] [[CODE](https://github.com/ljaiverson/pgfed)] - Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00240)] - A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation. [[PUB](https://doi.org/10.1109/ICCV51070.2023.01076)] - Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking. [[PUB](https://doi.org/10.1109/ICCV51070.2023.00914)] [[CODE](https://github.com/yolomax/ColTrack)] - GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization. [[PUB](https://doi.org/10.1109/ICCV51070.2023.00458)] - HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning. [[PUB](https://doi.org/10.1109/ICCV51070.2023.01898)] - Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning. [[PUB](https://doi.org/10.1109/ICCV51070.2023.02045)] #### ICCV workshop - Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10350693)] [[PDF](https://arxiv.org/abs/2310.01366)] - Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10350429)] - FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. [[PUB](https://ieeexplore.ieee.org/document/10350898)] [[CODE](https://github.com/chatzikon/FedRCIL)] - FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. [[PUB](https://ieeexplore.ieee.org/document/10350371)] #### CVPR - Rethinking Federated Learning With Domain Shift: A Prototype View. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html)] [[CODE](https://github.com/WenkeHuang/RethinkFL)] - Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html)] [[CODE](https://github.com/minglllli/CBAFed)] - DaFKD: Domain-Aware Federated Knowledge Distillation. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html)] [[CODE](https://github.com/haozhaowang/DaFKD2023)] - The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.14868)] - FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Miao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html)] - On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Li_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https://arxiv.org/abs/2212.02191)] - Elastic Aggregation for Federated Optimization. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html)] - FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https://arxiv.org/abs/2207.09653)] - Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html)] - ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html)] [[CODE](https://github.com/git-disl/scale-fl)] - Reliable and Interpretable Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html)] - Federated Domain Generalization With Generalization Adjustment. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html)] [[CODE](https://github.com/MediaBrain-SJTU/FedDG-GA)] - Make Landscape Flatter in Differentially Private Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.11242)] [[CODE](https://github.com/YMJS-Irfan/DP-FedSAM)] - Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html)] [[PDF](https://arxiv.org/abs/2305.12557)] [[CODE](https://github.com/junyizhu-ai/confidence_aware_pfl)] - STDLens: Model Hijacking-Resilient Federated Learning for Object Detection. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Chow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.11511)] [[CODE](https://github.com/git-disl/STDLens)] - Re-Thinking Federated Active Learning Based on Inter-Class Diversity. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.12317)] [[CODE](https://github.com/raymin0223/LoGo)] - Learning Federated Visual Prompt in Null Space for MRI Reconstruction. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.16181)] [[CODE](https://github.com/chunmeifeng/FedPR)] - Fair Federated Medical Image Segmentation via Client Contribution Estimation. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Fair_Federated_Medical_Image_Segmen

AI & Machine Learning ML Frameworks
2K Github Stars