Awesome-FL
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
https://github.com/youngfish42/Awesome-FL
Last synced: 4 days ago
JSON representation
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fl in top cv conference and journal
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- [PUB - Client_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2311.13250)] [[CODE](https://github.com/innovator-zero/FedHCA2)] |
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- [PUB - efficient_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.14737)] |
- [PUB - Efficient_Federated_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2201.03172)] [[CODE](https://github.com/geehokim/FedACG)] |
- [PUB - 1311/FCD)] |
- [PUB - graph_Aggregation_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/MM-Fed/HAMFL)] |
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- [PUB - Precision_Quantization_for_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2311.18129)] |
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- [PUB - Enhanced_Data-free_Approach_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.14101)] [[CODE](https://github.com/tmtuan1307/lander)] |
- [PUB - Efficient_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2302.06637)] [[CODE](https://github.com/NVlabs/PerAda)] |
- [PUB - Gihun/FedSOL)] |
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- [PUB - Wise_Federated_Network_CVPR_2024_supplemental.pdf)] |
- [PUB - robust_Decentralized_Federated_CVPR_2024_supplemental.pdf)] |
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- [PUB - Efficient_Scheme_CVPR_2024_supplemental.zip)] [[PDF](https://arxiv.org/abs/2403.15760)] [[CODE](https://github.com/tsingz0/fedktl)] [[POSTER](https://github.com/TsingZ0/FedKTL/blob/main/FedKTL.png)] [[SLIDES](https://github.com/TsingZ0/FedKTL/blob/main/FedKTL.pdf)] |
- [PUB - Free_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2404.18962)] |
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- ICCV
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- MM - trier.de/db/conf/mm/mm2022.html), [2021](https://2021.acmmm.org/main-track-list), [2020](https://2020.acmmm.org/main-track-list.html)
- IJCV
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- CVPR - trier.de/db/conf/iccv/index.html)(IEEE International Conference on Computer Vision), [ECCV](https://dblp.uni-trier.de/db/conf/eccv/index.html)(European Conference on Computer Vision), [MM](https://dblp.org/db/conf/mm/index.html)(ACM International Conference on Multimedia), [IJCV](https://dblp.uni-trier.de/db/journals/ijcv/index.html)(International Journal of Computer Vision).
- CVPR
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- MM - trier.de/db/conf/mm/mm2022.html), [2021](https://2021.acmmm.org/main-track-list), [2020](https://2020.acmmm.org/main-track-list.html)
- IJCV
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- [PUB - Han/FEDCPA)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Han_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf)] |
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- [PUB - Net)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhang_Generative_Gradient_Inversion_ICCV_2023_supplemental.pdf)] |
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- [PUB - Talkie_Accelerating_Federated_ICCV_2023_supplemental.pdf)] |
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- [PUB - DAWA_Layer-wise_Divergence_ICCV_2023_supplemental.pdf)] |
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- [PUB - AI/EasyFL/tree/master/applications/mas)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhuang_MAS_Towards_Resource-Efficient_ICCV_2023_supplemental.pdf)] |
- [PUB - based_ICCV_2023_supplemental.pdf)] |
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- [PUB - metrics_against_backdoors_in_FL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Huang_Multi-Metrics_Adaptively_Identifies_ICCV_2023_supplemental.pdf)] |
- [PUB - 2023-fedetf)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Li_No_Fear_of_ICCV_2023_supplemental.pdf)] |
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- [PUB - Long-tailed-Learning)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zeng_Global_Balanced_Experts_ICCV_2023_supplemental.pdf)] |
- [PUB - Aware_Federated_Active_ICCV_2023_supplemental.pdf)] |
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- [PUB - Experiments)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Hu_Federated_Learning_Over_ICCV_2023_supplemental.pdf)] |
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- [PUB - ai/MultigraphFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Do_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_supplemental.pdf)] |
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- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2203.06338)] [[CODE](https://github.com/guopengf/Auto-FedRL)] |
- [PUB - FL-main)] [[VIDEO](https://www.youtube.com/watch?v=Ae1CDi0_Nok&ab_channel=StanfordMedAI)] |
- [PUB - Group/MaT-FL)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2203.11834)] [[CODE](https://github.com/debcaldarola/fedsam)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2204.13170)] [[CODE](https://github.com/varnio/fedsim)] [[PAGE](https://fedsim.varnio.com/en/latest/)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2207.09413)] |
- [PUB - rehman/fedvssl)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2203.14936)] [[CODE](https://github.com/eric-ai-lab/FedVLN)] |
- [PUB - Stage_Federated_CVPR_2022_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2204.04677)] [[CODE](https://github.com/xu-jingyi/fedcorr)] [[VIDEO](https://www.youtube.com/watch?v=GA22ct1LgRA&ab_channel=ZihanChen)] |
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- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2207.09158)] [[CODE](https://github.com/sungwon-han/fedx)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2207.04655)] [[CODE](https://github.com/jcwang123/fedlc)] |
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- [PUB - Based_Active_CVPR_2022_supplemental.zip)] [[PDF](http://arxiv.org/abs/2103.13822)] |
- [PUB - Wised_Model_Aggregation_CVPR_2022_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2205.03993)] |
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- [PUB - Workshop-CVPR2022/Becking_FSFL_FedVision_CVPR22.pdf)] [[VIDEO](https://youtu.be/A9nEWqGriZ4)] |
- [PUB - Workshop-CVPR2022/mpaf.pdf)] [[VIDEO](https://youtu.be/H3fetWD_ZHw)] |
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- [PUB - Workshop-CVPR2022/presentation-%20Zhengquan%20Luo.pdf)] [[VIDEO](https://youtu.be/bRMeXncAjWY)] |
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- [PUB - 2024/supplemental/Dutto_Collaborative_Visual_Place_CVPRW_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2404.13324)] |
- [PUB - 2024/supplemental/Gao_FedProK_Trustworthy_Federated_CVPRW_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.02685)] |
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- [PUB - ntu/FedReID)] [[解读](https://zhuanlan.zhihu.com/p/265987079)] |
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fl on graph data and graph neural networks
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-  and [Awesome-Federated-Machine-Learning](https://github.com/innovation-cat/Awesome-Federated-Machine-Learning#16-graph-neural-networks).
- [PDF - yao/FedGCN)] |
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- [PUB - icml.github.io/2021/papers/FL-ICML21_paper_74.pdf)] |
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- ![dblp - trier.de/search?q=Federated%20graph%7Csubgraph%7Cgnn)
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- [PDF - AI/FedGraphNN)] [[解读](https://zhuanlan.zhihu.com/p/429220636)] |
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- DBLP - Federated-Learning-on-Graph-and-GNN-papers](https://github.com/huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers) and [Awesome-Federated-Machine-Learning](https://github.com/innovation-cat/Awesome-Federated-Machine-Learning#16-graph-neural-networks).
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Private Graph Neural Networks (todo)
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fl on tabular data
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Private Graph Neural Networks (todo)
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- [PUB - ijcai2019-workshop.pdf)] [[CODE](https://github.com/FederatedAI/FATE/tree/master/python/federatedml/ensemble/secureboost)] [[解读](https://zhuanlan.zhihu.com/p/545739311)] [[UC](https://github.com/Koukyosyumei/AIJack)] |
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workshops
-
secret sharing
- [FL@FM-IJCAI'24
- [FL@FM-ICME'24 - Media, Niagara Falls, ON, Canada
- [FL@FM-TheWebConf'24
- [FL@FM-Singapore'24
- [TTIC Chicago Summer Workshop
- [Federated and Collaborative Learning
- [FL@FM-AJCAI'23
- [FL-KDD'23 - located with the 29th ACM SIGKDD Conference (KDD 2023), Long Beach, CA, USA
- [FL@FM-NeurIPS'23 - NeurIPS’23), New Orleans, LA, USA
- [FL-IJCAI'23 - IJCAI'23), Macau
- [FLW@TheWebConf'23
- [CIKM'22
- [FL-ICML'23
- [FLIRT-SIGIR'23
- [FLSys'23
- [AI Technology School 2022
- [FL-CVPR'22
- [FL-NeurIPS'22
- [FL-MobiCom'22 - Research Track, Sydney, Australia
- [FL-AAAI-22
- [The Federated Learning Workshop, 2021
- [PDFL-EMNLP'21
- [FTL-IJCAI'21
- [DeepIPR-IJCAI'21
- [FL-ICML'21
- [RSEML-AAAI-21
- [NeurIPS-SpicyFL'20
- [FL-IJCAI'20
- [FL-IBM'20
- [FL-IJCAI'19
- [FL-Google'19
- [FLIRT-SIGIR'23
- [FL-NeurIPS'22
- [FL-IJCAI'22
- [FL-AAAI-22
- [DeepIPR-IJCAI'21
- [FL-ICML'21
- [RSEML-AAAI-21
- [NeurIPS-SpicyFL'20
- [FL-IJCAI'19
- [2nd MBZUAI Workshop
- [FL-CVPR'23
- [FL-NeurIPS'21
- [NeurIPS-SpicyFL'20
- [FL-IJCAI'20
-
-
journal special issues
-
secret sharing
- Special Issue on Trustworthy Federated Learning
- Special Issue on Trustable, Verifiable, and Auditable Federated Learning
- Special Issue on Federated Learning: Algorithms, Systems, and Applications
- Special Issue on Federated Machine Learning
- Special Issue on Trustable, Verifiable, and Auditable Federated Learning
- Special Issue on Federated Machine Learning
-
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fl in top-tier journal
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- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
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- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
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- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
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- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
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- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
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- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - 021-01506-3#code-availability)] |
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- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
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- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
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- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
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- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
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- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
- [PUB - jiang/Lancelot)] |
- [PUB
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- [PUB - 11/DynamicFL)] |
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- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
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- [PUB - challenge)] |
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- [PUB - 11/DynamicFL)] |
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- [PUB - AI/Front-End)] |
- [PUB
- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
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- [PUB
- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
- [PUB - jiang/Lancelot)] |
- [PUB
- [PUB
- [PUB
- [PUB - challenge)] |
- [PUB
- [PUB
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- [PUB - 11/DynamicFL)] |
- [PUB
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- [PUB - Group/MatSwarm)] |
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- [PUB
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- [PUB
- [PUB
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- [PUB - labs/ProxyFL)] |
- [PUB
- [PUB
- [PUB - AI/Front-End)] |
- [PUB
- [PUB - 722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] |
- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
- [PUB
- [PUB - jiang/Lancelot)] |
- [PUB
- [PUB
- [PUB
- [PUB - challenge)] |
- [PUB
- [PUB
- [PUB
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- [PUB - 11/DynamicFL)] |
- [PUB
- [PUB
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- [PUB - labs/ProxyFL)] |
- [PUB
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- [PUB - AI/Front-End)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
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- [PUB - 11/DynamicFL)] |
- [PUB
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- [PUB - Group/MatSwarm)] |
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- [PUB
- [PUB
- [PUB - AI/Front-End)] |
- [PUB
- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
- [PUB
- [PUB
- [PUB
- [PUB - challenge)] |
- [PUB
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- [PUB - 11/DynamicFL)] |
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- [PUB
- [PUB
- [PUB - EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
- [PUB
- [PUB - EIC-AI-LAB/UCADI)] |
- [PUB - 021-01506-3#code-availability)] |
- [PUB
- [PUB - learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] |
-
fl in top ml conference and journal
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Umu/Federated-Unlearning-under-Plausible-Deniability)] |
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Umu/Federated-Unlearning-under-Plausible-Deniability)] |
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - a-neural-tangent-kernelbased-framework-for-federated-learning-analysis)] |
- [PUB - ICML21)] [[VIDEO](https://www.youtube.com/watch?v=tKDbc60XJks&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https://hongliny.github.io/files/FCO_ICML21/FCO_ICML21_slides.pdf)] |
- [PUB - shared-representations-for-personalized-federated-learning)] |
- [PUB - knowledge-distillation-for-heterogeneous-federated-learning)] |
- [PUB - continual-learning-with-weighted-interclient-transfer)] |
- [PUB - deep-auc-maximization-for-hetergeneous-data-with-a-constant-communication-complexity)] |
- [PUB - reduced-local-sgd-for-less-heterogeneous-federated-learning)] |
- [PUB - learning-of-user-verification-models-without-sharing-embeddings)] |
- [PUB - Federated-Learning/tree/clustered_sampling)] [[VIDEO](https://slideslive.com/38959618/clustered-sampling-lowvariance-and-improved-representativity-for-clients-selection-in-federated-learning)] |
- [PUB - fair-and-robust-federated-learning-through-personalization)] |
- [PUB - for-the-win-oneshot-federated-clustering)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=5L66DZpPSHk&name=other_supplementary_material)] |
- [PUB - supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=7ypu4_en3Zm&name=other_supplementary_material)] [[CODE](https://github.com/zfan20/PFGNNPlus)] |
- [PUB - supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=Q06wKxnHRv&name=other_supplementary_material)] |
- [PUB - supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=gSMiXJmMEOf&name=other_supplementary_material)] [[CODE](https://github.com/matenure/federated_feature_fusion)] |
- [PUB
- [PUB - LAQ)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- NeurIPS - trier.de/db/conf/icml/index.html)(International Conference on Machine Learning), [ICLR](https://dblp.uni-trier.de/db/conf/iclr/index.html)(International Conference on Learning Representations), [COLT](https://dblp.org/db/conf/colt/index.html)(Annual Conference Computational Learning Theory) , [UAI](https://dblp.org/db/conf/uai/index.html)(Conference on Uncertainty in Artificial Intelligence),[Machine Learning](https://dblp.org/db/journals/ml/index.html), [JMLR](https://dblp.uni-trier.de/db/journals/jmlr/index.html)(Journal of Machine Learning Research), [TPAMI](https://dblp.uni-trier.de/db/journals/pami/index.html)(IEEE Transactions on Pattern Analysis and Machine Intelligence).
- NeurIPS - accept-oral)), [2022](https://papers.nips.cc/paper_files/paper/2022)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2022/Conference)), [2021](https://papers.nips.cc/paper/2021)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2021/Conference)), [2020](https://papers.nips.cc/paper/2020), [2018](https://papers.nips.cc/paper/2018), [2017](https://papers.nips.cc/paper/2017)
- ICML
- ICLR
- COLT
- UAI
- Machine Learning
- JMLR
- TPAMI
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- [PUB
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- [PUB - 0C83/)] |
- [PUB - CZOFO)] |
- [PUB
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- [PUB
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- [PUB - Minimax-and-Conditional-Stochastic-Optimization/tree/main)] |
- [PUB
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- [PUB - yao/FedGCN)] |
- [PUB
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- [PUB
- [PUB
- [PUB - CO2)] |
- [PUB - Minimax-and-Conditional-Stochastic-Optimization/)] |
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- [PUB
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- [PUB
- [PUB - research/dataset_grouper)] |
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- [PUB
- [PUB
- [PUB - supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=JtSlA972EHP&name=other_supplementary_material)] [[PDF](https://arxiv.org/abs/2208.05135)] |
- [PUB - supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=Gt_GiNkBhu&name=other_supplementary_material)] [[CODE](https://github.com/wrh14/learning_to_invert)] |
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- [PUB
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- [PUB
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- [PUB - stochastic-federataed-bandit)] |
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - PUB)] |
- [PUB
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- [PUB
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- [PUB - SJTU/FedDisco)] |
- [PUB
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- [PUB
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- [PUB - 2023-fedlaw)] |
- [PUB - 2023/Slides/24679_ljO6pDE.pdf)] |
- [PUB
- [PUB
- [PUB
- [PUB - ai/icml2023_fedxl)] |
- [PUB
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- [PUB - 2023/Slides/23569.pdf)] |
- [PUB - sri/tableak)] |
- [PUB
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- [PUB
- [PUB
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- [PUB - durable-backdoor)] |
- [PUB
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- [PUB - 2023/Slides/25109.pdf)] |
- [PUB
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- [PUB - yet-Equal-CML)] |
- [PUB
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- [PUB
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- [PUB - a/byzantine-gas)] |
- [PUB
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- [PUB - market-via-adaptive-sampling)] |
- [PUB
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- [PUB - FL/FedLab)] |
- [PUB - wang/moml)] |
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- [PUB - Federated-Learning/tree/asynchronous_FL)] |
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- [PUB
- [PUB - Privacy-Federated-Representation-Learning)] |
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- [PUB - clustering-of-bandits)] |
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- [PUB - pagh/private-countsketch)] |
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- [PUB - optimal-federated)] |
- [PUB
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- [PUB - MLSys-Lab/FedRolex)] |
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- [PUB
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- [PUB
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- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB - disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix)] [[CODE](https://github.com/gdisag/gradient_disaggregation)] |
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- [PUB - Incentives)] [[VIDEO](https://slideslive.com/38959135/one-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning)] |
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- [PUB - for-compressed-gradient-descent-in-distributed-optimization)] |
- [PUB - federated-neural-matching)] |
- [PUB - group/ModelPoisoning)] |
- [PUB
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- [PUB
- [PUB
- [PUB
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- [PUB
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- [PUB
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- [PUB
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- [PUB
- NeurIPS - accept-oral)), [2023](https://papers.nips.cc/paper_files/paper/2023)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2023/Conference#tab-accept-oral)), [2022](https://papers.nips.cc/paper_files/paper/2022)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2022/Conference)), [2021](https://papers.nips.cc/paper/2021)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2021/Conference)), [2020](https://papers.nips.cc/paper/2020), [2018](https://papers.nips.cc/paper/2018), [2017](https://papers.nips.cc/paper/2017)
- ICML
- ICLR
- Machine Learning
- JMLR
- TPAMI
-
fl in top system conference and journal
- [PUB
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- [PUB
- [PUB
- OSDI - trier.de/db/journals/tpds/index.html)(IEEE Transactions on Parallel and Distributed Systems), [DAC](https://dblp.uni-trier.de/db/conf/dac/index.html)(Design Automation Conference), [TOCS](https://dblp.uni-trier.de/db/journals/tocs/index.html)(ACM Transactions on Computer Systems), [TOS](https://dblp.uni-trier.de/db/journals/tos/index.html)(ACM Transactions on Storage), [TCAD](https://dblp.uni-trier.de/db/journals/tcad/index.html)(IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems), [TC](https://dblp.uni-trier.de/db/journals/tc/index.html)(IEEE Transactions on Computers).
- OSDI
- SOSP
- ISCA
- MLSys
- EuroSys - papers.html), 2022, 2021, 2020
- TPDS
- DAC
- TOCS
- TOS
- TCAD
- TC
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Computing/FedTree)] |
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FL)] |
- [PUB
- [PUB
- [PUB - FL)] |
- [PUB
- [PUB - FedML-Experiments)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - optimal-federated-learning)] |
- [PUB
- [PUB - codes/pFedSD)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FL)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - grp/DONE)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - For-Personalised-DNNs)] |
- [PUB
- [PUB
- [PUB
- [PUB - FedAvg)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Balancing-Federated-Learning)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- OSDI
- SOSP
- ISCA
- MLSys
- EuroSys - papers.html), [2024](https://2024.eurosys.org/accepted-papers.html), [2023](https://2023.eurosys.org/accepted-papers.html), 2022, 2021, 2020
- TPDS
- DAC
- TOCS
- TOS
- TCAD
- TC
- [PUB
- [PUB - eval-in-fl)] |
- [PUB
- [PUB
- [PUB
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- [PUB - codes/pFedSD)] |
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-
federated learning framework
-
benchmark
-
table
- FedLearn - algo.svg?color=blue)](https://github.com/fedlearnAI/fedlearn-algo/stargazers)<br /> | [Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform](https://arxiv.org/abs/2107.04129) | JD | | | |
- OpenFed - commit/FederalLab/OpenFed) | [OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework](https://arxiv.org/abs/2109.07852) | | | | [[DOC](https://openfed.readthedocs.io/README.html)] |
- Federated-Learning-source - ETH/Federated-Learning-source.svg?color=blue)](https://github.com/MTC-ETH/Federated-Learning-source/stargazers)<br /> | [A Practical Federated Learning Framework for Small Number of Stakeholders](https://dl.acm.org/doi/10.1145/3437963.3441702) | ETH Zürich | | | [[DOC](https://github.com/MTC-ETH/Federated-Learning-source/blob/master/dashboard/README.md)] |
- PySyft - commit/OpenMined/PySyft) | [A generic framework for privacy preserving deep learning](https://arxiv.org/abs/1811.04017) | [OpenMined](https://www.openmined.org/) | | | [[DOC](https://pysyft.readthedocs.io/en/latest/installing.html)] |
- FATE - commit/FederatedAI/FATE) | [FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection](https://www.jmlr.org/papers/volume22/20-815/20-815.pdf) | [WeBank](https://fedai.org/) | | :white_check_mark::white_check_mark: | [[DOC](https://fate.readthedocs.io/en/latest/)] [[DOC(ZH)](https://fate.readthedocs.io/en/latest/zh/)] |
- Flower - commit/adap/flower) | [Flower: A Friendly Federated Learning Research Framework](https://arxiv.org/abs/2104.03042.pdf) | [flower.ai](https://flower.ai/) | | | [[DOC](https://flower.ai/docs/)] |
- FedML - AI/FedML.svg?color=red)](https://github.com/FedML-AI/FedML/stargazers)<br /> | [FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518) | [FedML](https://fedml.ai/) | :white_check_mark::white_check_mark: | :white_check_mark: | [[DOC](https://doc.fedml.ai/)] |
- SecretFlow - commit/secretflow/secretflow) | | [Ant group](https://www.antgroup.com/) | | :white_check_mark: | [[DOC](https://secretflow.readthedocs.io/en/latest/getting_started/index.html)] |
- PFLlib - commit/TsingZ0/PFLlib) | [PFLlib: Personalized Federated Learning Algorithm Library](https://arxiv.org/abs/2312.04992) | SJTU | | | [[PAGE](http://www.pfllib.com/)] |
- FederatedScope - commit/alibaba/FederatedScope) | [FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://www.vldb.org/pvldb/vol16/p1059-li.pdf) | [Alibaba DAMO Academy](https://damo.alibaba.com/labs/data-analytics-and-intelligence) | :white_check_mark::white_check_mark: | | [[DOC](https://federatedscope.io/refs/index)] [[PAGE](https://federatedscope.io/)] |
- Primihub - commit/primihub/primihub) | | [primihub](https://github.com/primihub) | | | [[DOC]()] |
- Fedlearner - commit/bytedance/fedlearner) | | [Bytedance](https://github.com/bytedance) | | | |
- LEAF - commit/TalwalkarLab/leaf) | [LEAF: A Benchmark for Federated Settings](https://arxiv.org/abs/1812.01097.pdf) | [CMU](https://leaf.cmu.edu/) | | | |
- OpenFL - commit/intel/openfl) | [OpenFL: An open-source framework for Federated Learning](https://arxiv.org/abs/2105.06413) | [Intel](https://github.com/intel) | | | [[DOC](https://openfl.readthedocs.io/en/latest/install.html)] |
- Fedlab - FL/FedLab.svg?color=blue)](https://github.com/SMILELab-FL/FedLab/stargazers)<br /> | [FedLab: A Flexible Federated Learning Framework](https://jmlr.org/papers/v24/22-0440.html) | [SMILELab](https://github.com/SMILELab-FL/) | | | [[DOC](https://fedlab.readthedocs.io/en/master/)] [[DOC(ZH)](https://fedlab.readthedocs.io/zh_CN/latest/)] [[PAGE](https://github.com/SMILELab-FL/FedLab-benchmarks)] |
- NVFlare - commit/NVIDIA/NVFlare) | [NVIDIA FLARE: Federated Learning from Simulation to Real-World](http://sites.computer.org/debull/A23mar/p170.pdf) | [NVIDIA](https://github.com/NVIDIA) | | | [[DOC](https://nvflare.readthedocs.io/en/2.1.1/)] |
- Privacy Meter - commit/privacytrustlab/ml_privacy_meter) | [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://ieeexplore.ieee.org/document/8835245) | University of Massachusetts Amherst | | | |
- NIID-Bench - Computing/NIID-Bench.svg?color=blue)](https://github.com/Xtra-Computing/NIID-Bench/stargazers)<br /> | [Federated Learning on Non-IID Data Silos: An Experimental Study](https://arxiv.org/abs/2102.02079.pdf) | [Xtra Computing Group](https://github.com/Xtra-Computing) | | | |
- FLGo - commit/WwZzz/easyFL) | [Federated Learning with Fair Averaging](https://www.ijcai.org/proceedings/2021/223)<br />[FLGo: A Fully Customizable Federated Learning Platform](https://arxiv.org/abs/2306.12079) | XMU | | | |
- Rosetta - Foundation/Rosetta.svg?color=blue)](https://github.com/LatticeX-Foundation/Rosetta/stargazers)<br /> | | [matrixelements](https://www.matrixelements.com/product/rosetta) | | | [[DOC](https://github.com/LatticeX-Foundation/Rosetta/blob/master/doc/DEPLOYMENT.md)] [[PAGE](https://github.com/LatticeX-Foundation/Rosetta)] |
- PaddleFL - commit/PaddlePaddle/PaddleFL) | | Baidu | | | [[DOC](https://paddlefl.readthedocs.io/en/latest/index.html)] |
- IBM Federated Learning - learning-lib.svg?color=blue)](https://github.com/IBM/federated-learning-lib/stargazers)<br /> | [IBM Federated Learning: an Enterprise Framework White Paper](https://arxiv.org/abs/2007.10987.pdf) | [IBM](https://github.com/IBM) | | :white_check_mark: | [[PAPERS](https://github.com/IBM/federated-learning-lib/blob/main/docs/papers.md)] |
- KubeFATE - commit/FederatedAI/KubeFATE) | | [WeBank](https://fedai.org/) | | | [[WIKI](https://github.com/FederatedAI/KubeFATE/wiki/#faqs)] |
- FedScale - commit/SymbioticLab/FedScale) | [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://arxiv.org/abs/2105.11367.pdf) | [SymbioticLab(U-M)](https://symbioticlab.org/) | | | |
- PersonalizedFL - commit/microsoft/PersonalizedFL) | | microsoft | | | |
- Differentially Private Federated Learning: A Client-level Perspective - samples/machine-learning-diff-private-federated-learning.svg?color=blue)](https://github.comSAP-samples/machine-learning-diff-private-federated-learning/stargazers)<br /> | [Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557) | [SAP-samples](https://github.com/SAP-samples) | | | |
- plato - System/plato.svg?color=blue)](https://github.com/TL-System/plato/stargazers)<br /> | [Plato: An Open-Source Research Framework for Production Federated Learning](https://dl.acm.org/doi/10.1145/3603165.3607364) | UofT | | | |
- Backdoors 101 - commit/ebagdasa/backdoors101) | [Blind Backdoors in Deep Learning Models](https://arxiv.org/abs/2005.03823) | Cornell Tech | | | |
- SWARM LEARNING - learning.svg?color=blue)](https://github.com/HewlettPackard/swarm-learning/stargazers)<br /> | [Swarm Learning for decentralized and confidential clinical machine learning](https://www.nature.com/articles/s41586-021-03583-3) | | | | [[VIDEO](https://github.com/HewlettPackard/swarm-learning/blob/master/docs/videos.md)] |
- EasyFL - AI/EasyFL.svg?color=blue)](https://github.com/EasyFL-AI/EasyFL/stargazers)<br /> | [EasyFL: A Low-code Federated Learning Platform For Dummies](https://ieeexplore.ieee.org/abstract/document/9684558) | NTU | | | |
- Breaching - commit/JonasGeiping/breaching) | A Framework for Attacks against Privacy in Federated Learning ([papers](https://github.com/JonasGeiping/breaching)) | | | | |
- substra - commit/Substra/substra) | | [Substra](https://github.com/Substra) | | | [[DOC](https://doc.substra.ai/index.html)] |
- FedJAX - commit/google/fedjax) | [FEDJAX: Federated learning simulation with JAX](https://arxiv.org/abs/2108.02117.pdf) | [Google](https://ai.googleblog.com/2021/10/fedjax-federated-learning-simulation.html) | | | |
- FLSim - commit/facebookresearch/FLSim) | | [facebook research ](https://github.com/facebookresearch) | | | |
- Galaxy Federated Learning - commit/GalaxyLearning/GFL) | [GFL: A Decentralized Federated Learning Framework Based On Blockchain](https://arxiv.org/abs/2010.10996.pdf) | ZJU | | | [[DOC](http://galaxylearning.github.io/)] |
- FedNLP - AI/FedNLP.svg?color=blue)](https://github.com/FedML-AI/FedNLP/stargazers)<br /> | [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://arxiv.org/abs/2104.08815) | [FedML](https://fedml.ai/) | | | |
- PyVertical - commit/OpenMined/PyVertical) | [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://arxiv.org/abs/2104.00489.pdf) | [OpenMined](https://www.openmined.org/) | | | |
- FLSim - commit/iQua/flsim) | [Optimizing Federated Learning on Non-IID Data with Reinforcement Learning](https://ieeexplore.ieee.org/document/9155494/) | University of Toronto | | | |
- Xaynet - commit/xaynetwork/xaynet) | | [XayNet](https://www.xayn.com/) | | | [[PAGE](https://www.xaynet.dev/)] [[DOC](https://docs.rs/xaynet)] [[WHITEPAPER](https://uploads-ssl.webflow.com/5f0c5c0bb18a279f0a62919e/5f157004da6585f299fa542b_XayNet%20Whitepaper%202.1.pdf)] [[LEGAL REVIEW](https://uploads-ssl.webflow.com/5f0c5c0bb18a279f0a62919e/5fcfa8e3389ecc84a9309513_XAIN%20Legal%20Review%202020%20v1.pdf)] |
- SyferText - commit/OpenMined/SyferText) | | [OpenMined](https://www.openmined.org/) | | | |
- FedTorch - commit/MLOPTPSU/FedTorch) | [Distributionally Robust Federated Averaging](https://papers.nips.cc/paper/2020/file/ac450d10e166657ec8f93a1b65ca1b14-Paper.pdf) | Penn State | | | |
- FLUTE - commit/microsoft/msrflute) | [FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations](https://arxiv.org/abs/2203.13789) | microsoft | | | [[DOC](https://microsoft.github.io/msrflute/)] |
- FedGraphNN - AI/FedGraphNN.svg?color=blue)](https://github.com/FedML-AI/FedGraphNN/stargazers)<br /> | [FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://arxiv.org/abs/2104.07145) | [FedML](https://fedml.ai/) | :white_check_mark::white_check_mark: | | |
- FEDn - commit/scaleoutsystems/fedn) | [Scalable federated machine learning with FEDn](https://ieeexplore.ieee.org/document/9826069/) | [scaleoutsystems](http://www.scaleoutsystems.com) | | | [[DOC](https://scaleoutsystems.github.io/fedn/)] |
- FedTree - Computing/FedTree.svg?color=blue)](https://github.com/Xtra-Computing/FedTree/stargazers)<br /> | [FedTree: A Federated Learning System For Trees](https://proceedings.mlsys.org/paper_files/paper/2023/hash/3430e7055936cb8e26451ed49fce84a6-Abstract-mlsys2023.html) | [Xtra Computing Group](https://github.com/Xtra-Computing) | | :white_check_mark::white_check_mark: | [[DOC](https://fedtree.readthedocs.io/en/latest/index.html)] |
- PhotoLabeller - commit/mccorby/PhotoLabeller) | | | | | [[BLOG](https://proandroiddev.com/federated-learning-e79e054c33ef)] |
- FATE-Serving - Serving.svg?color=blue)](https://github.com/FederatedAI/FATE-Serving/stargazers)<br /> | | [WeBank](https://fedai.org/) | | | [[DOC](https://fate-serving.readthedocs.io/en/develop/)] |
- PriMIA - commit/gkaissis/PriMIA) | [End-to-end privacy preserving deep learning on multi-institutional medical imaging](https://www.nature.com/articles/s42256-021-00337-8) | [TUM](https://www.tum.de/en/); Imperial College London; [OpenMined](https://www.openmined.org) | | | [[DOC](https://g-k.ai/PriMIA/)] |
- APPFL - commit/APPFL/APPFL) | [APPFL: open-source software framework for privacy-preserving federated learning](https://ieeexplore.ieee.org/document/9835407/) | | | | [[DOC](https://appfl.readthedocs.io/en/stable/)] |
- FeTS - AI/Front-End.svg?color=blue)](https://github.com/FETS-AI/Front-End/stargazers)<br /> | [The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research](http://iopscience.iop.org/article/10.1088/1361-6560/ac9449) | [Federated Tumor Segmentation (FeTS) initiative](https://www.med.upenn.edu/cbica/fets/) | | | [[DOC](https://fets-ai.github.io/Front-End/)] |
- FedCV - AI/FedCV.svg?color=blue)](https://github.com/FedML-AI/FedCV/stargazers)<br /> | [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://arxiv.org/abs/2111.11066) | FedML | | | |
- MPLC - learning-contributivity.svg?color=blue)](https://github.com/LabeliaLabs/distributed-learning-contributivity/stargazers)<br /> | | [LabeliaLabs](https://github.com/LabeliaLabs) | | | [[PAGE](https://www.labelia.org)] |
- Flame - open/flame.svg?color=blue)](https://github.com/cisco-open/flame/stargazers)<br /> | [Flame: Simplifying Topology Extension in Federated Learning](https://dl.acm.org/doi/10.1145/3620678.3624665) | Cisco | | | [[DOC](https://fedsim.varnio.com/en/latest/)] |
- FlexCFL - commit/morningD/FlexCFL) | [Flexible Clustered Federated Learning for Client-Level Data Distribution Shift](https://arxiv.org/abs/2108.09749) | Chongqing University | | | |
- FedGroup - commit/morningD/GrouProx) | [FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure](https://arxiv.org/abs/2010.06870) | Chongqing University | | | |
- FedEval - Chai/FedEval.svg?color=blue)](https://github.com/Di-Chai/FedEval/stargazers)<br /> | [FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning](https://arxiv.org/abs/2011.09655) | HKU | | | [[DOC](https://di-chai.github.io/FedEval/)] |
- UCADI - EIC-AI-LAB/UCADI.svg?color=blue)](https://github.com/HUST-EIC-AI-LAB/UCADI/stargazers)<br /> | [Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence](https://www.nature.com/articles/s42256-021-00421-z) | Huazhong University of Science and Technology | | | |
- FedSim - commit/varnio/fedsim) | | | | | |
- GOLF - commit/IntelligentSystemsLab/generic_and_open_learning_federator) | | SYSU | | | [[DOC](https://generic-and-open-learning-federator.readthedocs.io/en/latest/)] |
- Federated-Learning-source - ETH/Federated-Learning-source.svg?color=blue)](https://github.com/MTC-ETH/Federated-Learning-source/stargazers)<br /> | [A Practical Federated Learning Framework for Small Number of Stakeholders](https://dl.acm.org/doi/10.1145/3437963.3441702) | ETH Zürich | | | [[DOC](https://github.com/MTC-ETH/Federated-Learning-source/blob/master/dashboard/README.md)] |
- Clara
- TFF(Tensorflow-Federated) - commit/tensorflow/federated) | [Towards Federated Learning at Scale: System Design](https://proceedings.mlsys.org/paper_files/paper/2019/hash/7b770da633baf74895be22a8807f1a8f-Abstract.html) | Google | | | [[DOC](https://www.tensorflow.org/federated)] [[PAGE](https://www.tensorflow.org/federated)] |
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fl in top ai conference and journal
- [PUB
- [PUB
- [PUB - cF_g/view?usp=drive_link)] |
- [PUB
- [PUB
- [PUB - Federated-Learning/tree/SIFU)] |
- [PUB
- [PUB
- [PUB - KLMS)] |
- [PUB - lab/On-DemandFL)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - of-Federated-Learning)] |
- [PUB - tsoy98/mutually-beneficial-federated-learning-replication)] |
- [PUB
- [PUB - Wang/InvariantAggregator)] |
- [PUB
- [PUB - robust-federated-learning)] |
- [PUB
- [PUB
- IJCAI - 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
- 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
- ALT
- AI
- [PUB
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- [PUB - secure-aggregation-mitigating-multiround-privacy-leakage-in-federated-learning)] [[CODE](https://openreview.net/attachment?id=nVV6S2sb_UL&name=supplementary_material)] |
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- [PUB
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- [PUB - minimax)] |
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- [PUB - flip-federated-backdoor-attack)] |
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - kim/pFedDef_v1)] |
- [PUB - federated-learning)] |
- [PUB - NonConvex-Federated-Learning-Without-a-Trusted-Server)] |
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB - cho22a/jee-cho22a-supp.zip)] |
- [PUB - supp.zip)] |
- [PUB - bounds-for-fedavg-and-continuous-perspective)] |
- [PUB
- [PUB
- [PUB - LinUCB)] |
- [PUB - admm)] |
- [PUB - OGUAieNY&ab_channel=FederatedLearningOneWorldSeminar)] |
- [PUB - Privacy-for-Heterogeneous-Federated-Learning)] |
- [PUB
- [PUB
- [PUB - supp.zip)] [[VIDEO](https://www.youtube.com/watch?v=fY8V184It1g&ab_channel=FederatedLearningOneWorldSeminar)] |
- [PUB - APPLE)] |
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- [PUB - FL)] |
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- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - bilevel-asynchronous-vertical-federated-learning-with-backward-updating)] |
- [PUB - lossless-federated-recommendation-with-explicit-feedback)] |
- [PUB - multiarmed-bandits)] |
- [PUB - the-convergence-of-communicationefficient-local-sgd-for-federated-learning)] |
- [PUB - differentially-private-federated-learning-in-the-shuffle-model)] [[CODE](https://github.com/Rachelxuan11/FLAME)] |
- [PUB - understanding-the-influence-of-individual-clients-in-federated-learning)] |
- [PUB
- [PUB - crosssilo-federated-learning-on-noniid-data)] [[UC.](https://github.com/TsingZ0/PFL-Non-IID)] |
- [PUB - games-analyzing-federated-learning-under-voluntary-participation)] |
- [PUB - or-redemption-how-data-heterogeneity-affects-the-robustness-of-federated-learning)] |
- [PUB - aaai21)] [[VIDEO](https://slideslive.com/38949109/game-of-gradients-mitigating-irrelevant-clients-in-federated-learning)] [[SUPP](https://github.com/nlokeshiisc/SFedAvg-AAAI21)] |
- [PUB - block-coordinate-descent-scheme-for-learning-global-and-personalized-models)] [[CODE](https://github.com/REIYANG/FedBCD)] |
- [PUB - against-backdoors-in-federated-learning-with-robust-learning-rate)] [[CODE](https://github.com/TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate)] |
- [PUB - Federated-Learning)] [[VIDEO](https://papertalk.org/papertalks/27640)] [[SUPP](http://proceedings.mlr.press/v130/fraboni21a/fraboni21a-supp.pdf)] |
- [PUB - fdp)] [[VIDEO](https://papertalk.org/papertalks/27595)] [[SUPP](http://proceedings.mlr.press/v130/zheng21a/zheng21a-supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB
- [PUB - Learning-Pytorch)] |
- [PUB - Computing/PrivML)] |
- [PUB - 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)] |
- [PUB - air/HarmoFL)] [[解读](https://zhuanlan.zhihu.com/p/472555067)] |
- [PUB
- [PUB
- [PUB - Structural-Federated-Learning)] |
- [PUB
- [PUB - federated-partial-label-learning-with-local-adaptive-augmentation-and-regularization)] |
- [PUB - no-prejudice-fair-federated-graph-neural-networks-for-personalized-recommendation)] [[PDF](https://arxiv.org/abs/2312.10080)] [[CODE](https://github.com/anujksirohi/F2PGNN-AAAI24)] |
- [PUB
- [PUB - 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)] |
- [PUB - fairtrade-achieving-pareto-optimal-trade-offs-between-balanced-accuracy-and-fairness-in-federated-learning)] |
- [PUB - combating-data-imbalances-in-federated-semi-supervised-learning-with-dual-regulators)] [[PDF](https://arxiv.org/abs/2307.05358)] |
- [PUB - fed-qssl-a-framework-for-personalized-federated-learning-under-bitwidth-and-data-heterogeneity)] [[PDF](https://arxiv.org/abs/2312.13380)] |
- [PUB
- [PUB - 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)] |
- [PUB - watch-your-head-assembling-projection-heads-to-save-the-reliability-of-federated-models)] [[PDF](https://arxiv.org/abs/2402.16255)] |
- [PUB - fedgcr-achieving-performance-and-fairness-for-federated-learning-with-distinct-client-types-via-group-customization-and-reweighting)] [[CODE](https://github.com/celinezheng/fedgcr)] |
- [PUB - 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)] |
- [PUB - exploiting-label-skews-in-federated-learning-with-model-concatenation)] [[PDF](https://arxiv.org/abs/2312.06290)] [[CODE](https://github.com/sjtudyq/FedConcat)] |
- [PUB - complementary-knowledge-distillation-for-robust-and-privacy-preserving-model-serving-in-vertical-federated-learning)] |
- [PUB - federated-learning-via-input-output-collaborative-distillation)] [[PDF](https://arxiv.org/abs/2312.14478)] [[CODE](https://github.com/lsl001006/fediod)] |
- [PUB - 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)] |
- [PUB - Guo/FedCSL)] |
- [PUB - fedfixer-mitigating-heterogeneous-label-noise-in-federated-learning)] [[PDF](https://arxiv.org/abs/2403.16561)] |
- [PUB - fedlps-heterogeneous-federated-learning-for-multiple-tasks-with-local-parameter-sharing)] [[PDF](https://arxiv.org/abs/2402.08578)] [[CODE](https://github.com/jyzgh/FedLPS)] |
- [PUB
- [PUB - performative-federated-learning-a-solution-to-model-dependent-and-heterogeneous-distribution-shifts)] |
- [PUB - general-commerce-intelligence-glocally-federated-nlp-based-engine-for-privacy-preserving-and-sustainable-personalized-services-of-multi-merchants)] |
- [PUB - emgan-early-mix-gan-on-extracting-server-side-model-in-split-federated-learning)] [[CODE](https://github.com/zlijingtao/SFL-MEA)] |
- [PUB - feddiv-collaborative-noise-filtering-for-federated-learning-with-noisy-labels)] [[PDF](https://arxiv.org/abs/2312.12263)] [[CODE](https://github.com/lijichang/FLNL-FedDiv)] |
- [PUB - 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)] |
- [PUB
- [PUB - federated-x-armed-bandit)] [[PDF](https://arxiv.org/abs/2205.15268)] [[CODE](https://github.com/williamlwj/pyxab)] |
- [PUB
- [PUB - ufda-universal-federated-domain-adaptation-with-practical-assumptions)] [[PDF](https://arxiv.org/abs/2311.15570)] [[CODE](https://github.com/Xinhui-99/UFDA)] |
- [PUB - fedasmu-efficient-asynchronous-federated-learning-with-dynamic-staleness-aware-model-update)] [[PDF](https://arxiv.org/abs/2312.05770)] |
- [PUB - language-guided-transformer-for-federated-multi-label-classification)] [[PDF](https://arxiv.org/abs/2312.07165)] [[CODE](https://github.com/Jack24658735/FedLGT)] |
- [PUB - fedcd-federated-semi-supervised-learning-with-class-awareness-balance-via-dual-teachers)] [[CODE](https://github.com/YunzZ-Liu/FedCD/)] |
- [PUB - beyond-traditional-threats-a-persistent-backdoor-attack-on-federated-learning)] [[CODE](https://github.com/PhD-TaoLiu/FCBA)] |
- [PUB - federated-learning-with-extremely-noisy-clients-via-negative-distillation)] [[PDF](https://arxiv.org/abs/2312.12703)] [[CODE](https://github.com/linChen99/FedNed)] |
- [PUB - 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)] |
- [PUB
- [PUB - a-primal-dual-algorithm-for-hybrid-federated-learning)] [[PDF](https://arxiv.org/abs/2210.08106)] |
- [PUB - fedlf-layer-wise-fair-federated-learning)] |
- [PUB - towards-fair-graph-federated-learning-via-incentive-mechanisms)] [[PDF](https://arxiv.org/abs/2312.13306)] [[CODE](https://github.com/Chenglu0426/FairGraphFL)] |
- [PUB - towards-the-robustness-of-differentially-private-federated-learning)] |
- [PUB - 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)] |
- [PUB - integer-is-enough-when-vertical-federated-learning-meets-rounding)] |
- [PUB - clip-guided-federated-learning-on-heterogeneity-and-long-tailed-data)] [[PDF](https://arxiv.org/abs/2312.08648)] [[CODE](https://github.com/shijiangming1/CLIP2FL)] |
- [PUB - federated-adaptive-prompt-tuning-for-multi-domain-collaborative-learning)] [[PDF](https://arxiv.org/abs/2211.07864)] [[CODE](https://github.com/leondada/fedapt)] |
- [PUB - multi-dimensional-fair-federated-learning)] [[PDF](https://arxiv.org/abs/2312.05551)] |
- [PUB
- [PUB
- [PUB - fedcompetitors-harmonious-collaboration-in-federated-learning-with-competing-participants)] [[PDF](https://arxiv.org/abs/2312.11391)] |
- [PUB - z-signfedavg-a-unified-stochastic-sign-based-compression-for-federated-learning)] [[PDF](https://arxiv.org/abs/2302.02589)] |
- [PUB - data-disparity-and-temporal-unavailability-aware-asynchronous-federated-learning-for-predictive-maintenance-on-transportation-fleets)] |
- [PUB - federated-graph-learning-under-domain-shift-with-generalizable-prototypes)] |
- [PUB
- [PUB - concealing-sensitive-samples-against-gradient-leakage-in-federated-learning)] [[PDF](https://arxiv.org/abs/2209.05724)] [[CODE](https://github.com/JingWu321/DCS-2)] |
- [PUB - 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)] |
- [PUB - federated-causality-learning-with-explainable-adaptive-optimization)] [[PDF](https://arxiv.org/abs/2312.05540)] |
- [PUB - federated-contextual-cascading-bandits-with-asynchronous-communication-and-heterogeneous-users)] [[PDF](https://arxiv.org/abs/2402.16312)] |
- [PUB
- [PUB - diversity-authenticity-co-constrained-stylization-for-federated-domain-generalization-in-person-re-identification)] |
- [PUB - perfedrlnas-one-for-all-personalized-federated-neural-architecture-search)] |
- [PUB - efficient-asynchronous-federated-learning-with-prospective-momentum-aggregation-and-fine-grained-correction)] |
- [PUB
- [PUB - 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)] |
- [PUB - xfl)] |
- [PUB - a-huber-loss-minimization-approach-to-byzantine-robust-federated-learning)] [[PDF](https://arxiv.org/abs/2308.12581)] |
- [PUB - knowledge-aware-parameter-coaching-for-personalized-federated-learning)] |
- [PUB - federated-label-noise-learning-with-local-diversity-product-regularization)] [[SUPP](https://wanglab.sjtu.edu.cn/userfiles/files/Supp_FedLNL.pdf)] |
- [PUB - 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)] |
- [PUB
- [PUB - knowledge-transfer-via-compact-model-in-federated-learning-student-abstract)] |
- [PUB - picsr-prototype-informed-cross-silo-router-for-federated-learning-student-abstract)] |
- [PUB
- IJCAI
- 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
- ALT
- AI
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Guo/FedACD)] |
- [PUB - Guo/FedACD)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - dzd/FedPFT)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - class-imbalance-in-federated-learning)] [[CODE](https://github.com/balanced-fl/Addressing-Class-Imbalance-FL)] [[解读](https://zhuanlan.zhihu.com/p/443009189)] |
- [PUB - AI/SpreadGNN)] [[解读](https://zhuanlan.zhihu.com/p/429720860)] |
- [PUB - Federated-Learning)] [[VIDEO](https://papertalk.org/papertalks/27640)] [[SUPP](http://proceedings.mlr.press/v130/fraboni21a/fraboni21a-supp.pdf)] |
- [PUB - fdp)] [[VIDEO](https://papertalk.org/papertalks/27595)] [[SUPP](http://proceedings.mlr.press/v130/zheng21a/zheng21a-supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] |
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Sun/FL_FedAPA)] |
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FL)] |
- [PUB
- [PUB - Safwan/Fed-UCBVI)] |
- [PUB
- [PUB - RCTs-Khellaf/)] |
- [PUB - cost-of-local-and-global-fairness-in-FL)] |
- [PUB
- [PUB - richardson-romberg/supplementary.zip)] |
- [PUB - BPFL)] |
- [PUB
- [PUB
-
fl in top dm conference and journal
- [PUB
- KDD - trier.de/db/conf/wsdm/index.html)(Web Search and Data Mining).
- KDD - track-papers/), [Applied Data Science track](https://kdd.org/kdd2023/ads-track-papers/), [Workshop](https://fl4data-mining.github.io/papers/)), 2022([Research Track](https://kdd.org/kdd2022/paperRT.html), [Applied Data Science track](https://kdd.org/kdd2022/paperADS.html)), [2021](https://kdd.org/kdd2021/accepted-papers/index), [2020](https://www.kdd.org/kdd2020/accepted-papers)
- WSDM - conference.org/2023/program/accepted-papers), [2022](https://www.wsdm-conference.org/2022/accepted-papers/), [2021](https://www.wsdm-conference.org/2021/accepted-papers.php), [2019](https://www.wsdm-conference.org/2019/accepted-papers.php)
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - sw/f2l)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - knowcomp/uc-fedrec)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - DIVER/ShapleyFL-Robust-Federated-Learning-Based-on-Shapley-Value)] |
- [PUB
- [PUB - AUPRC)] |
- [PUB
- [PUB
- [PUB
- [PUB - PFL)] |
- [PUB
- [PUB - sail/fed-multimodal)] |
- [PUB
- [PUB - bench)] |
- [PUB - fedrec)] |
- [PUB - mining.github.io/)] |
- [PUB
- [PUB - to-collaborate)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Chai/FedEval)] |
- [PUB
- [PUB
- [PUB - LTD%20Towards%20Cross-Platform%20Ride%20Hailing%20via.pdf)] [[解读](https://zhuanlan.zhihu.com/p/544183874)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - ETH/Federated-Learning-source)] |
- [PUB - deep-knowledge-tracing)] |
- [PUB
- [PUB
- [PUB - es)] |
- KDD - track-papers/), [Applied Data Science track](https://kdd.org/kdd2023/ads-track-papers/), [Workshop](https://fl4data-mining.github.io/papers/)), 2022([Research Track](https://kdd.org/kdd2022/paperRT.html), [Applied Data Science track](https://kdd.org/kdd2022/paperADS.html)), [2021](https://kdd.org/kdd2021/accepted-papers/index), [2020](https://www.kdd.org/kdd2020/accepted-papers)
- WSDM - conference.org/2025/accepted-papers/), [2024](https://www.wsdm-conference.org/2024/accepted-papers/), [2023](https://www.wsdm-conference.org/2023/program/accepted-papers), [2022](https://www.wsdm-conference.org/2022/accepted-papers/), [2021](https://www.wsdm-conference.org/2021/accepted-papers.php), [2019](https://www.wsdm-conference.org/2019/accepted-papers.php)
-
fl in top secure conference and journal
- S&P - security.org/program-papers.html), [2024](https://sp2024.ieee-security.org/program-papers.html), [2023](https://sp2023.ieee-security.org/program-papers.html), [2022](https://www.ieee-security.org/TC/SP2022/program-papers.html), [2019](https://www.ieee-security.org/TC/SP2019/program-papers.html)
- CCS - papers.html), [2021](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [2019](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/), [2017](https://acmccs.github.io/papers/)
- USENIX Security - sessions), [2022](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [2020](https://www.usenix.org/conference/usenixsecurity20/technical-sessions)
- NDSS - symposium.org/ndss2025/accepted-papers/), [2024](https://www.ndss-symposium.org/ndss2024/accepted-papers/), [2023](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [2022](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [2021](https://www.ndss-symposium.org/ndss2021/accepted-papers/)
- [PUB
- [PUB
- [PUB
- S&P - trier.de/db/conf/ccs/index.html)(Conference on Computer and Communications Security), [USENIX Security](https://dblp.uni-trier.de/db/conf/uss/index.html)(Usenix Security Symposium) and [NDSS](https://dblp.uni-trier.de/db/conf/ndss/index.html)(Network and Distributed System Security Symposium).
- S&P - security.org/program-papers.html), [2022](https://www.ieee-security.org/TC/SP2022/program-papers.html), [2019](https://www.ieee-security.org/TC/SP2019/program-papers.html)
- CCS - papers.html), [2021](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [2019](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/), [2017](https://acmccs.github.io/papers/)
- USENIX Security - sessions), [2022](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [2020](https://www.usenix.org/conference/usenixsecurity20/technical-sessions)
- NDSS - symposium.org/ndss2023/accepted-papers/), [2022](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [2021](https://www.ndss-symposium.org/ndss2021/accepted-papers/)
- [PUB
- [PUB
- [PUB - YUE/rog)] |
- [PUB
- [PUB - Sec/FRM)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - lab/rofl-project-code)] |
- [PUB
- [PUB
- [PUB
- [PUB - chandran.pdf)] |
- [PUB - stevens.pdf)] [[VIDEO](https://www.youtube.com/watch?v=9kYHQkr6DuE)] |
- [PUB - fu-chong.pdf)] [[CODE](https://github.com/FuChong-cyber/label-inference-attacks)] [[VIDEO](https://www.youtube.com/watch?v=JEmRbDtosVw)] |
- [PUB - nguyen.pdf)] [[PDF](https://arxiv.org/abs/2101.02281)] [[VIDEO](https://www.youtube.com/watch?v=nMrte2S9U68)] |
- [PUB - POgQulyX2vzKzUtZEkVn1M9G2a&index=3)] [[UC.](https://github.com/wenzhu23333/Differential-Privacy-Based-Federated-Learning)] |
- [PUB - POgQsS08uHJUJI6sawDO_3sNh0&index=3)] [[UC.](https://github.com/cyberthreat-datasets/ctdd-2021-os-syslogs)] |
- [PUB - POgQs8ZZMMCX1RoNnmSQ70QXxd&index=3)] |
- [PUB - POgQulyX2vzKzUtZEkVn1M9G2a&index=4)] |
- [PUB - POgQvaqlGPwlOa0JR3bryB1KCS&index=2)] [[SLIDE](https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf)] |
- [PUB - PMzxZ3c&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=1)] |
- [PUB - Model-Poisoning)] [[VIDEO](https://www.youtube.com/watch?v=G2VYRnLqAXE&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=3)] |
- [PUB
- [PUB
- [PUB
- [PUB - security.org/TC/SP2019/SP19-Slides-pdfs/Milad_Nasr_-_08-Milad_Nasr-Comprehensive_Privacy_Analysis_of_Deep_Learning_)] [[CODE](https://github.com/privacytrustlab/ml_privacy_meter)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - MIA)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - dario/eludingsecureaggregation)] |
- [PUB - Maddock/federated-boosted-dp-trees)] |
- [PUB
- [PUB
- [PUB
- [PUB - Junbao/SecureAggregation)] [[UC](https://github.com/corentingiraud/federated-learning-secure-aggregation)] |
-
fl in top nlp conference and journal
- ACL - trier.de/db/conf/naacl/index.html)(North American Chapter of the Association for Computational Linguistics), [EMNLP](https://dblp.uni-trier.de/db/conf/emnlp/index.html)(Conference on Empirical Methods in Natural Language Processing) and [COLING](https://dblp.uni-trier.de/db/conf/coling/index.html)(International Conference on Computational Linguistics).
- ACL - 2023/), [2022](https://aclanthology.org/events/acl-2022/), [2021](https://aclanthology.org/events/acl-2021/), [2019](https://aclanthology.org/events/acl-2019/)
- NAACL - 2022/), [2021](https://aclanthology.org/events/naacl-2021/)
- EMNLP - 2023/), [2022](https://aclanthology.org/events/emnlp-2022/), [2021](https://aclanthology.org/events/emnlp-2021/), [2020](https://aclanthology.org/events/emnlp-2020/)
- COLING - 2020/)
- [PUB - eff/FedPepTAO)] |
- [PUB - Multimodal-Complaint-Detection)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - nlp-group/fl4semanticparsing)] |
- [PUB - FL/FedLegal)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FL/FedVocab)] |
- [PUB
- [PUB - fedseit)] |
- [PUB
- [PUB
- [PUB
- [PUB - active-personalized-federated-learning)] |
- [PUB - AI/FedNLP)] |
- [PUB
- [PUB - mixed-domain-translation-models-via-federated-learning)] [[PDF](https://arxiv.org/abs/2205.01557)] |
- [PUB - federated-learning)] |
- [PUB - nlp/GCASeg)] |
- [PUB - FedRec)] [[VIDEO](https://aclanthology.org/2021.emnlp-main.223.mp4)] |
- [PUB - nlp/ASA-TM)] [[VIDEO](https://aclanthology.org/2021.emnlp-main.321.mp4)] |
- [PUB - main.606.mp4)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - phenotyping)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FL/FedVocab)] |
- [PUB - fedseit)] |
- [PUB - active-personalized-federated-learning)] |
- [PUB - mixed-domain-translation-models-via-federated-learning)] [[PDF](https://arxiv.org/abs/2205.01557)] |
- [PUB - federated-learning)] |
- [PUB - nlp/GCASeg)] |
- [PUB - FedRec)] [[VIDEO](https://aclanthology.org/2021.emnlp-main.223.mp4)] |
- [PUB - main.606.mp4)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - phenotyping)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- ACL - 2025/), [2024](https://aclanthology.org/events/acl-2024/), [2023](https://aclanthology.org/events/acl-2023/), [2022](https://aclanthology.org/events/acl-2022/), [2021](https://aclanthology.org/events/acl-2021/), [2019](https://aclanthology.org/events/acl-2019/)
- NAACL - 2024/), [2022](https://aclanthology.org/events/naacl-2022/), [2021](https://aclanthology.org/events/naacl-2021/)
- EMNLP - 2024/), [2023](https://aclanthology.org/events/emnlp-2023/), [2022](https://aclanthology.org/events/emnlp-2022/), [2021](https://aclanthology.org/events/emnlp-2021/), [2020](https://aclanthology.org/events/emnlp-2020/)
- COLING - main/), [2020](https://aclanthology.org/events/coling-2020/)
-
fl in top db conference and journal
- [PUB
- SIGMOD - trier.de/db/conf/icde/index.html)(IEEE International Conference on Data Engineering) and [VLDB](https://dblp.uni-trier.de/db/conf/vldb/index.html)(Very Large Data Bases Conference).
- SIGMOD
- ICDE - research-track/), [2022](https://icde2022.ieeecomputer.my/accepted-research-track/), [2021](https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding)
- VLDB - volume-info/), [2021](https://vldb.org/pvldb/vol15-volume-info/), [2021](http://www.vldb.org/pvldb/vol14/), [2020](http://vldb.org/pvldb/vol13-volume-info/)
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - SC)] |
- [PUB
- [PUB - Allen/FedGTA)] |
- [PUB
- [PUB
- [PUB - TEE)] |
- [PUB
- [PUB - 63CD/README.md)] |
- [PUB
- [PUB
- [PUB - edu/mpc4j/tree/main/mpc4j-sml-opboost)] |
- [PUB
- [PUB - mdc/FedTSC-FedST)] |
- [PUB
- [PUB
- [PUB
- [PUB - Computing/NIID-Bench)] |
- [PUB
- [PUB - demo)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - vfl)] |
- [PUB
- [PUB
- [PUB - db/FedRain-and-Frog)] |
- [PUB
- [PUB
- SIGMOD
- ICDE - icde.org/2025/research-papers/), [2024](https://icde2024.github.io/), [2023](https://icde2023.ics.uci.edu/papers-research-track/), [2022](https://icde2022.ieeecomputer.my/accepted-research-track/), [2021](https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding)
- VLDB - volume-info/), [2021](https://vldb.org/pvldb/vol15-volume-info/), [2021](http://www.vldb.org/pvldb/vol14/), [2020](http://vldb.org/pvldb/vol13-volume-info/)
- [PUB - websoft/FedChain)] |
- [PUB
- [PUB - Li/fl_auction)] |
- [PUB - SV)] |
- [PUB
- [PUB - totemdb/Teb-means)] |
- [PUB
- [PUB - ABY3)] |
- [PUB
- [PUB - Allen/OpenFGL)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - vfl)] |
- [PUB - AIMS/PFA)] |
- [PUB - db/FedRain-and-Frog)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
-
fl in top ir conference and journal
-
fl in top network conference and journal
- SIGCOMM
- SIGCOMM
- INFOCOM - infocom.org/program/accepted-paper-list-main-conference), [2022](https://infocom2022.ieee-infocom.org/program/accepted-paper-list-main-conference)([Page](https://infocom.info/day/3/track/Track%20B#B-7)), [2021](https://infocom2021.ieee-infocom.org/accepted-paper-list-main-conference.html)([Page](https://duetone.org/infocom21)), [2020](https://infocom2020.ieee-infocom.org/accepted-paper-list-main-conference.html)([Page](https://duetone.org/infocom20)), [2019](https://infocom2019.ieee-infocom.org/accepted-paper-list-main-conference.html), 2018
- MobiCom
- NSDI - accepted-papers), [Fall](https://www.usenix.org/conference/nsdi23/fall-accepted-papers))
- WWW - papers/), [2022](https://www2022.thewebconf.org/accepted-papers/), [2021](https://www2021.thewebconf.org/program/papers-program/links/index.html)
- [PUB - 8sY-Y)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - infocom23.pdf)] [[CODE](https://github.com/TL-System/plato/tree/main/examples/knot)] |
- [PUB
- [PUB
- [PUB - fed)] |
- [PUB
- [PUB
- [PUB - TR.pdf)] [[CODE](https://github.com/Distributed-Learning-Networking-Group/FedMoS/)] |
- [PUB
- [PUB
- [PUB
- [PUB - infocom23.pdf)] [[WEIBO](https://weibo.com/2174209470/MBt1Mofxv)] |
- [PUB
- [PUB - System/plato/)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl.pdf)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Salehi/FL-based-Sector-Selection)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Unlearning-via-Class-Discriminative-Pruning)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - HAR)] |
- [PUB
- [PUB - EF)] |
- [PUB
- [PUB - Chengxu/FLASH)] |
- [PUB
- [PUB
- [PUB
- [PUB - zh/TrustFL)] |
- [PUB
- [PUB - INFOCOM)] |
- [PUB
- [PUB
- SIGCOMM
- INFOCOM - infocom.org/program/accepted-paper-list-main-conference), [2024](https://infocom2024.ieee-infocom.org/program/accepted-paper-list-main-conference), [2023](https://infocom2023.ieee-infocom.org/program/accepted-paper-list-main-conference), [2022](https://infocom2022.ieee-infocom.org/program/accepted-paper-list-main-conference)([Page](https://infocom.info/day/3/track/Track%20B#B-7)), [2021](https://infocom2021.ieee-infocom.org/accepted-paper-list-main-conference.html)([Page](https://duetone.org/infocom21)), [2020](https://infocom2020.ieee-infocom.org/accepted-paper-list-main-conference.html)([Page](https://duetone.org/infocom20)), [2019](https://infocom2019.ieee-infocom.org/accepted-paper-list-main-conference.html), 2018
- MobiCom
- NSDI - sessions), 2023([Spring](https://www.usenix.org/conference/nsdi23/spring-accepted-papers), [Fall](https://www.usenix.org/conference/nsdi23/fall-accepted-papers))
- WWW - tracks/), [2023](https://www2023.thewebconf.org/program/accepted-papers/), [2022](https://www2022.thewebconf.org/accepted-papers/), [2021](https://www2021.thewebconf.org/program/papers-program/links/index.html)
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - srinivas.pdf)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - h7/PAGE)] |
- [PUB
- [PUB
- [PUB
- [PUB - fedrec)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - release/cgd)] |
- [PUB
- [PUB
- [PUB
- [PUB - learning.org/fl@fm-www-2024/)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - ju/VFedTrans)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - websoft/FedLU)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
-
fl in top conference and journal other fields
-
fl datasets
-
benchmark
- IEEE Communications Surveys & Tutorials 2020
- IEEE TIST 2019
- LEAF
- Federated AI Dataset
- ICML Workshop 2020
- IEEE Commun. Mag. 2020
- China Communications 2020
- Federated Learning Systems - 3-030-70604-3_2)]
- WorldS4 2020
- IEEE Internet Things J. 2022
- IEEE Communications Surveys & Tutorials 2020
- IEEE Communications Surveys & Tutorials 2020
- IEEE Signal Process. Mag. 2020
- IEEE Commun. Mag. 2020
- IEEE TKDE 2021
- IJCAI Workshop 2020
- Foundations and Trends in Machine Learning 2021 - 083)
- J. Heal. Informatics Res. 2021 - 020-00082-4)
- IEEE Communications Surveys & Tutorials 2020
- Ad Hoc Networks 2024 - Privacy-Computing-in-Metaverse)
- Federated-Learning
- ACM Trans. Interact. Intell. Syst.
-
-
fl graph datasets
-
benchmark
-
-
tutorials
-
benchmark
- NeurIPS 2020 - learning-tutorial)
- Federated Learning on MNIST using a CNN - CUqCsM))
- AAAI 2019
- Applied Cryptography
- A Brief Introduction to Differential Privacy
- Deep Learning with Differential Privacy.
- Building Safe A.I.
- Private Image Analysis with MPC
- Private Deep Learning with MPC
- NeurIPS 2020 - learning-tutorial)
- AAAI 2019
- 联邦学习入门教程参考
- Deep Learning with Differential Privacy.
- Private Image Analysis with MPC
- Private Deep Learning with MPC
-
-
course
-
conference special tracks
-
update log
-
secret sharing
- ACL
- innovation-cat - Federated-Machine-Learning](https://github.com/innovation-cat/Awesome-Federated-Machine-Learning) and find :fire: papers(code is available & stars >= 100)*
- Github Pages
-
-
citation
-
secret sharing
-
-
acknowledgments
-
secret sharing
- Awesome-GNN-Research
- Awesome-Federated-Learning-on-Graph-and-GNN-papers
- Awesome-Federated-Machine-Learning
- Awesome-Federated-Learning
- awesome-federated-learning
- FederatedAI research
- FLsystem-paper
- Federated Learning Framework Benchmark (UniFed)
- awesome-privacy-chinese
- anomaly-detection-resources
- awesome-image-registration
-
Categories
fl in top-tier journal
3,001
fl in top ml conference and journal
1,213
fl in top ai conference and journal
460
fl on graph data and graph neural networks
380
fl in top cv conference and journal
360
fl on tabular data
322
fl in top network conference and journal
242
fl in top system conference and journal
204
fl in top dm conference and journal
112
fl in top db conference and journal
96
fl in top nlp conference and journal
91
fl in top secure conference and journal
85
federated learning framework
64
workshops
45
fl datasets
27
fl in top ir conference and journal
22
fl in top conference and journal other fields
16
tutorials
15
acknowledgments
11
course
8
journal special issues
6
update log
4
conference special tracks
3
fl graph datasets
1
citation
1
Keywords
federated-learning
27
machine-learning
18
deep-learning
9
pytorch
8
privacy
6
federated-learning-framework
5
awesome-list
3
python
3
tensorflow
3
awesome
3
private-set-intersection
3
privacy-preserving
3
non-iid
3
privacy-preserving-machine-learning
3
secure-multiparty-computation
2
homomorphic-encryption
2
differential-privacy
2
distributed-learning
2
inference
2
scikit-learn
2
trusted-execution-environment
2
model-serving
2
data-privacy
2
gdpr
2
privacy-tools
2
distributed
2
decentralized-federated-learning
2
secure-computation
2
distributed-optimization
2
psi
2
communication-efficiency
2
android
2
security
2
federated-analytics
2
edge-computing
2
fleet-learning
2
distributed-computing
2
computer-vision
2
papers
2
vertical-federated-learning
2
edge-ai
2
mlsys
2
gloo
1
splitnn
1
split-neural-network
1
nccl
1
personalization
1
simulation
1
transformers-models
1
gnn
1