https://github.com/youngfish42/Awesome-FL

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
https://github.com/youngfish42/Awesome-FL
List: Awesome-FL
artificial-intelligence awesome computer-vision data-mining database deep-learning efficiency federated-learning federated-learning-framework graph graph-neural-networks information-retrieval knowledge-graph machine-learning natural-language-processing paper privacy security system tabular-data
Last synced: about 1 year ago
JSON representation
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
Host: GitHub
URL: https://github.com/youngfish42/Awesome-FL
Owner: youngfish42
License: cc-by-sa-4.0
Created: 2022-05-21T02:20:25.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2025-05-01T13:29:41.000Z (about 1 year ago)
Last Synced: 2025-05-01T14:35:30.705Z (about 1 year ago)
Topics: artificial-intelligence, awesome, computer-vision, data-mining, database, deep-learning, efficiency, federated-learning, federated-learning-framework, graph, graph-neural-networks, information-retrieval, knowledge-graph, machine-learning, natural-language-processing, paper, privacy, security, system, tabular-data
Language: Python
Homepage: https://youngfish42.github.io/Awesome-FL
Size: 5.11 MB
Stars: 1,697
Watchers: 40
Forks: 195
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

StarryDivineSky - youngfish42/Awesome-FL - FL是一个关于联邦学习的资源库，包含论文、框架、数据集、教程和研讨会等信息。它涵盖了联邦学习在不同领域的应用，例如人工智能、机器学习、数据挖掘、安全、计算机视觉、自然语言处理、信息检索、数据库、网络、系统等。该项目还提供了联邦学习在图数据和图神经网络以及表格数据上的资源。项目定期更新，用户可以通过 GitHub 提交问题或请求来建议其他关键资源。 (分布式机器学习)
ultimate-awesome - Awesome-FL - Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops). (Other Lists / TeX Lists)
awesome-awesome-artificial-intelligence - Awesome Federated Learning Resources - Federated-Learning-on-Graph-and-Tabular-Data?style=social) | (Privacy & Safety)
README

          # 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://img.shields.io/badge/dynamic/json?label=dblp&query=%24.result.hits[%27%40total%27]&url=https%3A%2F%2Fdblp.org%2Fsearch%2Fpubl%2Fapi%3Fq%3DFederated%2520graph%257Csubgraph%257Cgnn%26format%3Djson%26h%3D1000)](https://dblp.uni-trier.de/search?q=Federated%20graph%7Csubgraph%7Cgnn) 

  - [FL on Tabular Data](#fl-on-tabular-data) [![dblp](https://img.shields.io/badge/dynamic/json?label=dblp&query=%24.result.hits[%27%40total%27]&url=https%3A//dblp.org/search/publ/api%3Fq%3Dfederate%2520tree%257Cboost%257Cbagging%257Cgbdt%257Ctabular%257Cforest%257CXGBoost%26format%3Djson%26h%3D1000)](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.

**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 @ ([im.young@foxmail.com](mailto:im.young@foxmail.com)). 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)

 Events 

| Venue                                                        | 2024-2020                                                    | before 2020                                                  |

| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |

| [IJCAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AIJCAI%3A) | [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) | [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) | [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) | 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) | [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)  | [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) | 24, 23, 22                                                   | -                                                            |

| [JMLR](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fjmlr%3A) (J) | 24, 23, 22                                                   | -                                                            |

| [TPAMI](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fpami%3A) (J) | 25, 24, 23, 22                                               | -                                                            |

| [KDD](https://dblp.uni-trier.de/search?q=federate%20venue%3AKDD%3A) | [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) | [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) | [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) | [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) | [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) | [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) | [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) | [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) | 24                                                           | -                                                            |

| [ACL](https://dblp.uni-trier.de/search?q=federate%20venue%3AACL%3A) | [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) | [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) | [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) | [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) | [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) | [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) | -                                                            | -                                                            |

| [INFOCOM](https://dblp.uni-trier.de/search?q=federate%20venue%3AINFOCOM%3A) | [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) | [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) | 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) | [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) | [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) | [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) | 25, 24, 23, 22, 21, 20                                       | -                                                            |

| [DAC](https://dblp.uni-trier.de/search?q=federate%20venue%3ADAC%3A) | 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) | 24, 23, 22, 21                                               | -                                                            |

| [TC](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftc%3A) | 25, 24, 23, 22, 21                                           | -                                                            |

| [ICSE](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Ficse%3A) | [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) | -                                                            | -                                                            |

**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.

fl in top-tier journal

|Title                                                           |    Affiliation    |    Venue                    |    Year    |    Materials|

| ------------------------------------------------------------ | ----------- | --------------------- | ---- | ------------------------------------------------------------ |

| Achieving flexible fairness metrics in federated medical imaging | CUHK | 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 | HIT | 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 | Lhasa Limited | 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 | Yale University; UCSD | 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 | PKU | 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 | USTB; NTU | 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 | HKU | 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 | Stanford University | 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 | KAUST | 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 | TUM; UvA | 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 | Jiangmen Central Hospital; Guilin University of Aerospace Technology; Guilin University of Electronic Technology; | 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 | HKUST | 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 | IQVIA Cancer Research BV | Nat. Med. (Comment) | 2024 | [[PUB](https://www.nature.com/articles/s41591-023-02715-8)] |

| Multi-client distributed blind quantum computation with the Qline architecture | Sapienza Università di Roma | 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 | USTC | 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 | Tsinghua University | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43883-y)] |

| Advocating for neurodata privacy and neurotechnology regulation | Columbia University | Nat. Protoc. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41596-023-00873-0)] |

| Federated benchmarking of medical artificial intelligence with MedPerf | IHU Strasbourg; University of Strasbourg; Dana-Farber Cancer Institute; Weill Cornell Medicine; Harvard T.H. Chan School of Public Health; MIT; Intel | 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 | Harvard Medical School; Broad Institute of Harvard and Massachusetts Institute of Technology; Dana-Farber Cancer Institute | 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 | THU | 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 | Layer 6 AI; University of Waterloo; Vector Institute | 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 | University of Hamburg | 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 | Owkin | 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 | University of Pennsylvania | 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 | Hugging Face | Nat. Mach. Intell. (Comment) | 2022 | [[PUB](https://www.nature.com/articles/s42256-022-00551-y)] |

| Federated disentangled representation learning for unsupervised brain anomaly detection | TUM | 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 | Stanford University; Greenstone Biosciences | 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 | THU | 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 | THU | 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 | XMU; NTU | 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 | University of Wollongong | Sci. Rep. | 2022 | [[PUB](https://www.nature.com/articles/s41598-022-12833-x)] |

| Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence | HUST | 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 | MGH radiology and Harvard Medical School | 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 | Imperial College London; TUM;  OpenMined | 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: | DZNE; University of Bonn; | 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 | TUM; Imperial College London; OpenMined | 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 | CUHK; Princeton University | 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 | RWTH Aachen University | 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: | TUM; Imperial College London; OpenMined | Nat. Mach. Intell.(Perspective) | 2020 | [[PUB](https://www.nature.com/articles/s42256-020-0186-1)] |

## 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) [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) [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) [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) 2023

fl in top ai conference and journal

|Title                                                           |    Affiliation                                                     |    Venue      |    Year    |    Materials|

| ------------------------------------------------------------ | ------------------------------------------------------------ | ------- | ---- | ------------------------------------------------------------ |

| Federated Multi-View Clustering via Tensor Factorization |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/438)] |

| Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/439)] |

| LG-FGAD: An Effective Federated Graph Anomaly Detection Framework |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/416)] |

| Federated Prompt Learning for Weather Foundation Models on Devices |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/638)] |

| Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/419)] |

| Unlearning during Learning: An Efficient Federated Machine Unlearning Method |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/446)] |

| Practical Hybrid Gradient Compression for Federated Learning Systems |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/458)] |

| Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection |  | IJCAI | 2024 | [[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 |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/457)] [[CODE](https://github.com/Xianjie-Guo/FedACD)] |

| Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/788)] |

| FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/501)] |

| DarkFed: A Data-Free Backdoor Attack in Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/491)] |

| Scalable Federated Unlearning via Isolated and Coded Sharding |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/503)] |

| Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/238)] |

| Label Leakage in Vertical Federated Learning: A Survey |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/902)] |

| The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/980)] |

| LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/515)] |

| EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/51)] |

| Knowledge Distillation in Federated Learning: A Practical Guide |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/905)] |

| FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/526)] |

| FedPFT: Federated Proxy Fine-Tuning of Foundation Models |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/531)] [[CODE](https://github.com/pzp-dzd/FedPFT)] |

| A Systematic Survey on Federated Semi-supervised Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/911)] |

| Intelligent Agents for Auction-based Federated Learning: A Survey |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/912)] |

| A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/552)] |

| Dual Calibration-based Personalised Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/551)] |

| Stakeholder-oriented Decision Support for Auction-based Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/972)] |

| Redefining Contributions: Shapley-Driven Federated Learning |  | IJCAI | 2024 | [[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 |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/919)] |

| From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching |  | IJCAI | 2024 | [[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 |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/585)] |

| FedFa: A Fully Asynchronous Training Paradigm for Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/584)] |

| FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/594)] |

| FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/599)] |

| Personalized Federated Learning for Cross-City Traffic Prediction |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/611)] |

| Federated Adaptation for Foundation Model-based Recommendations |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/603)] |

| BADFSS: Backdoor Attacks on Federated Self-Supervised Learning |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/61)] |

| Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning |  | IJCAI | 2024 | [[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 |  | IJCAI | 2024 | [[PUB](https://www.ijcai.org/proceedings/2024/632)] |

| BOBA: Byzantine-Robust Federated Learning with Label Skewness | UIUC | AISTATS | 2024 | [[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 | University of Virginia | AISTATS | 2024 | [[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 | Stanford University; Meta | AISTATS | 2024 | [[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 | Princeton University | AISTATS | 2024 | [[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 | INRIA | AISTATS | 2024 | [[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 | INRIA | AISTATS | 2024 | [[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 | École Polytechnique | AISTATS | 2024 | [[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 | NJU; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/huang24c.html)] [[PDF](https://arxiv.org/abs/2211.07303)] |

| Adaptive Compression in Federated Learning via Side Information | Stanford University; University of Padova | AISTATS | 2024 | [[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 | UNIST | AISTATS | 2024 | [[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 | CMU | AISTATS | 2024 | [[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 | Huawei | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/leconte24a.html)] [[PDF](https://arxiv.org/abs/2405.00017)] |

| Personalized Federated X-armed Bandit | Purdue University | AISTATS | 2024 | [[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 | University of Oxford | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/molaei24a.html)] [[CODE](https://github.com/AnshThakur/FL4HeterogenousEHRs)] |

| Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization | University of Virginia | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/shen24c.html)] [[PDF](https://arxiv.org/abs/2311.00944)] |

| Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters | Northwestern University | AISTATS | 2024 | [[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 | Sofia University | AISTATS | 2024 | [[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 | University of Florida | AISTATS | 2024 | [[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 | UIUC | AISTATS | 2024 | [[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 | ISTA | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/zakerinia24a.html)] [[PDF](https://arxiv.org/abs/2206.10032)] [[CODE](https://github.com/ShayanTalaei/QuAFL)] |

| FedMut: Generalized Federated Learning via Stochastic Mutation | NTU | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29146)] |

| Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization | Carleton University | AAAI | 2024 | [[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 | IIT | AAAI | 2024 | [[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/anujksirohi/F2PGNN-AAAI24)] |

| Formal Logic Enabled Personalized Federated Learning through Property Inference | Vanderbilt University | AAAI | 2024 | [[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 | Illinois Tech | AAAI | 2024 | [[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 | Leibniz University | AAAI | 2024 | [[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 | HKUST | AAAI | 2024 | [[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 | UT | AAAI | 2024 | [[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 | University of Virginia | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29010)] |

| FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning | LMU Munich Siemens AG | AAAI | 2024 | [[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 | Xi'an Jiaotong University Shaanxi Joint Key Laboratory for Artificial Intelligence | AAAI | 2024 | [[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 | NTU | AAAI | 2024 | [[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 | Xiamen University | AAAI | 2024 | [[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 | NUS | AAAI | 2024 | [[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 | SUST; HKUST | AAAI | 2024 | [[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 | University at Buffalo; USA Harvard Medical School | AAAI | 2024 | [[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 | University of Waterloo Vector Institute | AAAI | 2024 | [[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 | HFUT | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29113)] [[PDF](https://github.com/Xianjie-Guo/FedCSL)] |

| FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning | Xi'an Jiaotong University;  Leiden University | AAAI | 2024 | [[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 | NJU | AAAI | 2024 | [[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 | TJU | AAAI | 2024 | [[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 | U-M | AAAI | 2024 | [[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 | Kyung Hee University;  Harex InfoTech | AAAI | 2024 | [[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 | Sony AI | AAAI | 2024 | [[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 | SYSU; HKU | AAAI | 2024 | [[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 | USTC; CAS | AAAI | 2024 | [[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 | CAS | AAAI | 2024 | [[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 | Purdue University | AAAI | 2024 | [[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 | GMU | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30291)] |

| UFDA: Universal Federated Domain Adaptation with Practical Assumptions | XJTU; University of Sydney | AAAI | 2024 | [[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 | Hithink RoyalFlush Information Network Co | AAAI | 2024 | [[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 | NTU | AAAI | 2024 | [[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 | SZU | AAAI | 2024 | [[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 | HEU | AAAI | 2024 | [[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 | XMU | AAAI | 2024 | [[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 | USTB | AAAI | 2024 | [[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 | USTC | AAAI | 2024 | [[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 | DCU | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30400)] |

| A Primal-Dual Algorithm for Hybrid Federated Learning | Northwestern University | AAAI | 2024 | [[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 | CUHK; Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29368)] [[PAGE](https://underline.io/lecture/93087-fedlf-layer-wise-fair-federated-learning)] |

| Towards Fair Graph Federated Learning via Incentive Mechanisms | ZJU; FDU | AAAI | 2024 | [[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 | THU | AAAI | 2024 | [[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 | ZJU; HUAWEI | AAAI | 2024 | [[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 | ZJU; Ant Group | AAAI | 2024 | [[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 | XMU | AAAI | 2024 | [[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 | FDU | AAAI | 2024 | [[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 | SDU | AAAI | 2024 | [[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 | ENN Group | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30317)] |

| On the Role of Server Momentum in Federated Learning | University of Virginia | AAAI | 2024 | [[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 | BUPT | AAAI | 2024 | [[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 | CUHK; China Shenzhen Research Institute of Big Data | AAAI | 2024 | [[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 | Volkswagen Group | AAAI | 2024 | [[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 | WHU | AAAI | 2024 | [[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 | Technical University of Munich | AAAI | 2024 | [[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 | TJU | AAAI | 2024 | [[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 | Monash University | AAAI | 2024 | [[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 | HUST | AAAI | 2024 | [[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 | SDU | AAAI | 2024 | [[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 | USTC | AAAI | 2024 | [[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 | FDU | AAAI | 2024 | [[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 | XMU; University of Trento | AAAI | 2024 | [[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 | U of T | AAAI | 2024 | [[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 | BUPT | AAAI | 2024 | [[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 | HKU | AAAI | 2024 | [[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 | SJTU | AAAI | 2024 | [[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 | NTU | AAAI | 2024 | [[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 | Zhejiang Lab | AAAI | 2024 | [[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 | Northeastern University | AAAI | 2024 | [[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 | SJTU | AAAI | 2024 | [[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) | UBC | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30557)] |

| Knowledge Transfer via Compact Model in Federated Learning (Student Abstract) | University of Sydney | AAAI | 2024 | [[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) | The Ohio State University Auton Lab, CMU | AAAI | 2024 | [[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)] |

| Privacy-preserving graph convolution network for federated item recommendation | SZU | AI | 2023 | [[PUB](https://www.sciencedirect.com/science/article/abs/pii/S000437022300142X)] |

| Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation | CAS; UCAS; JD Technology; JD Intelligent Cities Research | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25531)] |

| Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense | USTC; State Key Laboratory of Cognitive Intelligence | AAAI | 2023 | [[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 | SDU | AAAI | 2023 | [[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 | Lehigh University | AAAI | 2023 | [[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 | USC | AAAI | 2023 | [[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 | MSU | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25955)] |

| Complement Sparsification: Low-Overhead Model Pruning for Federated Learning | NJIT | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25977)] |

| Almost Cost-Free Communication in Federated Best Arm Identification | NUS | AAAI | 2023 | [[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 | University of Southern California Inha University | AAAI | 2023 | [[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 | BJTU | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26083)] |

| FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26122)] |

| Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning | USC | AAAI | 2023 | [[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 | UTS | AAAI | 2023 | [[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 | UCSD | AAAI | 2023 | [[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 | WHU; Hubei Luojia Laboratory; JD Explore Academy | AAAI | 2023 | [[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 | SUTD | AAAI | 2023 | [[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 | XMU | AAAI | 2023 | [[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 | University of Pittsburgh | AAAI | 2023 | [[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 | HKUST | AAAI | 2023 | [[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 | TJU | AAAI | 2023 | [[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 | ZJU | AAAI | 2023 | [[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 | GSU | AAAI | 2023 | [[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 | SUNY-Binghamton University | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26271)] |

| FedALA: Adaptive Local Aggregation for Personalized Federated Learning | SJTU | AAAI | 2023 | [[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 | ZJU | AAAI | 2023 | [[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 | TJU | AAAI | 2023 | [[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 | RUC; Engineering Research Center of Ministry of Education on Database and BI | AAAI | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26400)] [[PDF](https://arxiv.org/abs/2304.05516)] |

| DPAUC: Differentially Private AUC Computation in Federated Learning | ByteDance Inc. | AAAI Special Tracks | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26770)] [[PDF](https://arxiv.org/abs/2208.12294)] [[CODE](https://github.com/bytedance/fedlearner)] |

| Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout | NTU | AAAI Special Programs | 2023 | [[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 | KU Leuven | AAAI Special Programs | 2023 | [[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) | CNU | AAAI Special Programs | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26984)] |

| MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract) | PolyU | AAAI Special Programs | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26995)] |

| Clustered Federated Learning for Heterogeneous Data (Student Abstract) | RUC | AAAI Special Programs | 2023 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27049)] |

| FedSampling: A Better Sampling Strategy for Federated Learning | THU | IJCAI | 2023 | [[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 | ZJU | IJCAI | 2023 | [[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 | NTU | IJCAI | 2023 | [[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 | ZJU | IJCAI | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/245)] |

| Federated Graph Semantic and Structural Learning | WHU | IJCAI | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/426)] |

| BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning | SYSU | IJCAI | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/498)] [[PDF](https://arxiv.org/abs/2305.05221)] |

| FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment | SYSU | IJCAI | 2023 | [[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 | Webank | IJCAI | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/418)] [[PDF](https://arxiv.org/abs/2301.12623)] |

| Globally Consistent Federated Graph Autoencoder for Non-IID Graphs | FZU | IJCAI | 2023 | [[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 | NTU | IJCAI | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/474)] |

| Dual Personalization on Federated Recommendation | JLU; University of Technology Sydney | IJCAI | 2023 | [[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 | HUST | IJCAI | 2023 | [[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 | Xiangtan University | IJCAI | 2023 | [[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 | CUHK | IJCAI | 2023 | [[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 | Ping An Technology; THU | IJCAI | 2023 | [[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 | UTS | IJCAI | 2023 | [[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 | ZJU | IJCAI | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/483)] [[CODE](https://github.com/Hanzhouu/FedBFPT)] |

| Bayesian Federated Learning: A Survey |  | IJCAI Survey Track | 2023 | [[PDF](https://arxiv.org/abs/2304.13267)] |

| A Survey of Federated Evaluation in Federated Learning | Macquarie University | IJCAI Survey Track | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/758)] [[PDF](https://arxiv.org/abs/2305.08070)] |

| SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract) | INSA Centre Val de Loire | IJCAI Journal Track | 2023 | [[PUB](https://www.ijcai.org/proceedings/2023/772)] |

| The communication cost of security and privacy in federated frequency estimation | Stanford | AISTATS | 2023 | [[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 | Rice University | AISTATS | 2023 | [[PUB](https://proceedings.mlr.press/v206/dun23a.html)] [[CODE](https://github.com/dunchen/AsyncDrop__Release)] |

| Federated Learning under Distributed Concept Drift | CMU | AISTATS | 2023 | [[PUB](https://proceedings.mlr.press/v206/jothimurugesan23a.html)] [[CODE](https://github.com/microsoft/FedDrift)] |

| Characterizing Internal Evasion Attacks in Federated Learning | CMU | AISTATS | 2023 | [[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 | Stanford | AISTATS | 2023 | [[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 | USC | AISTATS | 2023 | [[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 | Universit ́ e Cˆ ote d’Azur | AISTATS | 2023 | [[PUB](https://proceedings.mlr.press/v206/marfoq23a.html)] [[CODE](https://github.com/omarfoq/streaming-fl)] |

| Nothing but Regrets — Privacy-Preserving Federated Causal Discovery | Helmholtz Centre for Information Security | AISTATS | 2023 | [[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 | UFL | AISTATS | 2023 | [[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 | CMAP | AISTATS | 2023 | [[PUB](https://proceedings.mlr.press/v206/plassier23a.html)] |

| Byzantine-Robust Federated Learning with Optimal Statistical Rates | UC Berkeley | AISTATS | 2023 | [[PUB](https://github.com/wanglun1996/secure-robust-federated-learning)] [[CODE](https://github.com/wanglun1996/secure-robust-federated-learning)] |

| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI | 2023 | [[PDF](https://arxiv.org/abs/2211.13009)] [[CODE](https://github.com/yuetan031/fedstar)] |

| FedGS: Federated Graph-based Sampling with Arbitrary Client Availability | XMU | AAAI | 2023 | [[PDF](https://arxiv.org/abs/2211.13975)] [[CODE](https://github.com/wwzzz/fedgs)] |

| Incentive-boosted Federated Crowdsourcing | SDU | AAAI | 2023 | [[PDF](https://arxiv.org/abs/2211.14439)] |

| Towards Understanding Biased Client Selection in Federated Learning. | CMU | AISTATS | 2022 | [[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 | KAUST | AISTATS | 2022 | [[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. | Stanford | AISTATS | 2022 | [[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. | PKU | AISTATS | 2022 | [[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 | Purdue | AISTATS | 2022 | [[PUB](https://proceedings.mlr.press/v151/ke22a.html)] [[PDF](https://arxiv.org/abs/2106.07255)] |

| Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. | University of Virginia | AISTATS | 2022 | [[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. | CMU | AISTATS | 2022 | [[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 | Meta AI | AISTATS | 2022 | [[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. | Stanford | AISTATS | 2022 | [[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 | Princeton | AISTATS | 2022 | [[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 | KAUST | AISTATS | 2022 | [[PUB](https://proceedings.mlr.press/v151/qian22a.html)] [[PDF](https://arxiv.org/abs/2111.01847)] |

| Federated Functional Gradient Boosting. | University of Pennsylvania | AISTATS | 2022 | [[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. | Criteo AI Lab | AISTATS | 2022 | [[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)] |

| Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting **`kg.`** | ZJU | IJCAI | 2022 | [[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 | UTS | IJCAI | 2022 | [[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 | ZJU | IJCAI | 2022 | [[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 |  | IJCAI | 2022 | [[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 |  | IJCAI | 2022 | [[PUB](https://www.ijcai.org/proceedings/2022/399)] [[PDF](https://arxiv.org/abs/2204.12703)] |

| Private Semi-Supervised Federated Learning. |  | IJCAI | 2022 | [[PUB](https://www.ijcai.org/proceedings/2022/279)] |

| Continual Federated Learning Based on Knowledge Distillation. |  | IJCAI | 2022 | [[PUB](https://doi.org/10.24963/ijcai.2022/306)] |

| Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features |  | IJCAI | 2022 | [[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 |  | IJCAI | 2022 | [[PUB](https://www.ijcai.org/proceedings/2022/475)] |

| Personalized Federated Learning with Contextualized Generalization. |  | IJCAI | 2022 | [[PUB](https://www.ijcai.org/proceedings/2022/311)] [[PDF](https://arxiv.org/abs/2106.13044)] |

| Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. |  | IJCAI | 2022 | [[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 |  | IJCAI | 2022 | [[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. |  | IJCAI | 2022 | [[PUB](https://www.ijcai.org/proceedings/2022/385)] [[PDF](https://arxiv.org/abs/2204.11536)] |

| Towards Verifiable Federated Learning **`surv.`** |  | IJCAI | 2022 | [[PUB](https://www.ijcai.org/proceedings/2022/792)] [[PDF](https://arxiv.org/abs/2202.08310)] |

| HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images | CUHK; BUAA | AAAI | 2022 | [[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 | BUPT | AAAI | 2022 | [[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 | USC | AAAI | 2022 | [[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 | HIT; PCL | AAAI | 2022 | [[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 | TJU | AAAI | 2022 | [[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 | SUNY-Binghamton University | AAAI | 2022 | [[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 | University of Pittsburgh | AAAI | 2022 | [[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 | UTS | AAAI | 2022 | [[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 | CMU | AAAI | 2022 | [[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 | The University of Texas at Austin | AAAI | 2022 | [[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 | National Taiwan University | AAAI | 2022 | [[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 | CSIRO | AAAI | 2022 | [[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 | Soochow University | AAAI | 2022 | [[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 | IIT Kanpur | AAAI | 2022 | [[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 | IBM Research | AAAI | 2022 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20775)] [[PDF](https://arxiv.org/abs/2112.07157)] [[CODE](https://github.com/rithram/flynn)] |

| Iterated Vector Fields and Conservatism, with Applications to Federated Learning. | Google | ALT | 2022 | [[PUB](https://proceedings.mlr.press/v167/charles22a.html)] [[PDF](https://arxiv.org/abs/2109.03973)] |

| Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization |  | IJCAI | 2021 | [[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 |  | IJCAI | 2021 | [[PUB](https://www.ijcai.org/proceedings/2021/352)] [[PDF](https://arxiv.org/abs/2106.12300)] |

| FedSpeech: Federated Text-to-Speech with Continual Learning |  | IJCAI | 2021 | [[PUB](https://www.ijcai.org/proceedings/2021/527)] [[PDF](https://arxiv.org/abs/2110.07216)] |

| Practical One-Shot Federated Learning for Cross-Silo Setting |  | IJCAI | 2021 | [[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 |  | IJCAI | 2021 | [[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 |  | IJCAI | 2021 | [[PUB](https://www.ijcai.org/proceedings/2021/217)] [[PDF](https://arxiv.org/abs/2007.15789)] |

| Federated Learning with Fair Averaging. :fire: |  | IJCAI | 2021 | [[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. |  | IJCAI | 2021 | [[PUB](https://www.ijcai.org/proceedings/2021/67)] [[PDF](https://arxiv.org/abs/2106.00275)] |

| Communication-efficient and Scalable Decentralized Federated Edge Learning. |  | IJCAI | 2021 | [[PUB](https://www.ijcai.org/proceedings/2021/720)] |

| Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating | Xidian University; JD Tech | AAAI | 2021 | [[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 | SZU | AAAI | 2021 | [[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 | University of Virginia | AAAI | 2021 | [[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 | Temple University; University of Pittsbu
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