Awesome-FL
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
https://github.com/youngfish42/Awesome-FL
Last synced: about 3 hours 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 - 1311/FCD)] |
- [PUB - Efficient_Federated_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2201.03172)] [[CODE](https://github.com/geehokim/FedACG)] |
- [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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- 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
- ICCV
- ECCV
- 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)] |
- [PUB - AIM-Group/FedPD)] |
- [PUB - jayzhang/Federated-Class-Continual-Learning)] |
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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 - disl/scale-fl)] |
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- [PUB - Irfan/DP-FedSAM)] |
- [PUB - ai/confidence_aware_pfl)] |
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- [PUB - against-grad-inversion-attacks)] |
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- [PUB - Group/MaT-FL)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2203.11834)] [[CODE](https://github.com/debcaldarola/fedsam)] |
- [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)] |
- [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)] |
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- [PUB - Based_Active_CVPR_2022_supplemental.zip)] [[PDF](http://arxiv.org/abs/2103.13822)] |
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- [PUB - supp.pdf)] |
- [PUB - supp.pdf)] [[PDF](https://arxiv.org/abs/2203.06338)] [[CODE](https://github.com/guopengf/Auto-FedRL)] |
- [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 - FL-main)] [[VIDEO](https://www.youtube.com/watch?v=Ae1CDi0_Nok&ab_channel=StanfordMedAI)] |
- [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)] |
- [PUB - MRCM)] |
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- [PUB - Preserving_Federated_Learning_ICCV_2021_paper.pdf)] |
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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 - Neural-Network-based-Federated-Learning-for-Heterogenous-Device-Network)] |
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- [PUB - fusion)] |
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- [PUB - icml.github.io/2021/papers/FL-ICML21_paper_74.pdf)] |
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- [PUB - HAR/Graph-Federated-Learning-for-CIoT-Devices.git)] |
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- [PUB - A-Graph-Neural-Network-Based-Federated-Learning-Approach-by-Hiding-Structure.pdf)] |
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- [PDF - AI/FedGraphNN)] [[解读](https://zhuanlan.zhihu.com/p/429220636)] |
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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 - Federated-Learning-via-Random-Forests)] |
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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
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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@FM-NeurIPS'23 - NeurIPS’23), New Orleans, LA, USA
- [FL-IJCAI'23 - IJCAI'23), Macau
- [FL-KDD'23 - located with the 29th ACM SIGKDD Conference (KDD 2023), Long Beach, CA, USA
- [CIKM'22
- [FL-ICML'23
- [FLIRT-SIGIR'23
- [FLSys'23
- [FLW@TheWebConf'23
- [AI Technology School 2022
- [FL-CVPR'22
- [FL-NeurIPS'22
- [FL-AAAI-22
- [FL-MobiCom'22 - Research Track, Sydney, Australia
- [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
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journal special issues
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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
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- [PUB
- [PUB
-
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- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
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- [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)] |
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- [PUB - learning-of-user-verification-models-without-sharing-embeddings)] |
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- [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)] |
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- [PUB
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- [PUB
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- [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
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- [PUB
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- [PUB
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- [PUB
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- [PUB
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- [PUB
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- [PUB
- [PUB
- [PUB - PUB)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - SJTU/FedDisco)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - 2023-fedlaw)] |
- [PUB - 2023/Slides/24679_ljO6pDE.pdf)] |
- [PUB
- [PUB
- [PUB
- [PUB - ai/icml2023_fedxl)] |
- [PUB
- [PUB - SJTU/pFedGraph)] |
- [PUB - 2023/Slides/23569.pdf)] |
- [PUB - sri/tableak)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - durable-backdoor)] |
- [PUB
- [PUB - lab/fedbr)] |
- [PUB - 2023/Slides/25109.pdf)] |
- [PUB
- [PUB - ai/surrogate_model_extension)] |
- [PUB - yet-Equal-CML)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - a/byzantine-gas)] |
- [PUB
- [PUB
- [PUB - market-via-adaptive-sampling)] |
- [PUB
- [PUB
- [PUB
- [PUB - FL/FedLab)] |
- [PUB - wang/moml)] |
- [PUB
- [PUB
- [PUB - Federated-Learning/tree/asynchronous_FL)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB - propagation)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Shi/FedCLS)] |
- [PUB
- [PUB
- [PUB - federated-learning-without-a-trusted-server)] |
- [PUB
- [PUB
- [PUB - thu/creamfl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - ML-Lab/episode)] |
- [PUB
- [PUB - Privacy-Federated-Representation-Learning)] |
- [PUB
- [PUB
- [PUB
- [PUB - training)] |
- [PUB
- [PUB
- [PUB - CST/iLRG)] |
- [PUB
- [PUB
- [PUB - &name=SUPP_material)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - clustering-of-bandits)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - pagh/private-countsketch)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - optimal-federated)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - MLSys-Lab/FedRolex)] |
- [PUB
- [PUB
- [PUB
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- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - vfl-codes)] |
- [PUB
- [PUB - SysML/FILM)] |
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Computing/FedSim)] |
- [PUB
- [PUB - ML-Lab/Federated-Sparse-Learning)] |
- [PUB
- [PUB - research/federated/tree/master/private_linear_compression)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17529.pdf)] |
- [PUB
- [PUB - dai/DisPFL)] |
- [PUB
- [PUB - 2022/Slides/16009.pdf)] [[CODE](https://media.icml.cc/Conferences/ICML2022/SUPP/honig22a-supp.zip)] |
- [PUB
- [PUB
- [PUB
- [PUB - torch)] |
- [PUB - SJTU/GAMF)] |
- [PUB
- [PUB - 2022/Slides/16926.pdf)] |
- [PUB - per)] |
- [PUB
- [PUB
- [PUB
- [PUB - 2022/Slides/17084.pdf)] |
- [PUB - 2022/Slides/17435.pdf)] |
- [PUB
- [PUB - nya/FedNest)] |
- [PUB - Distributed-Mean-Estimation)] |
- [PUB
- [PUB - 2022/Slides/16194_hmjFNsN.pdf)] [[CODE](https://github.com/a514514772/ProgFed)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - YUE/ntk-fed)] |
- [PUB
- [PUB - 2022/Slides/17302.pdf)] [[UC.](https://github.com/AllenBeau/pFedBayes)] |
- [PUB
- [PUB - Learning-Backdoor/)] |
- [PUB
- [PUB
- [PUB - sri/bayes-framework-leakage)] |
- [PUB
- [PUB
- [PUB
- [PUB - hw/FedPara_ICLR22)] |
- [PUB - Learning-Pytorch)] |
- [PUB
- [PUB
- [PUB
- [PUB - lab/divfl)] |
- [PUB - sam/FedOptim)] |
- [PUB - research/federated/tree/7525c36324cb022bc05c3fce88ef01147cae9740/periodic_distribution_shift)] |
- [PUB
- [PUB - AI/EasyFL)] |
- [PUB - research/federated/tree/master/generalization)] |
- [PUB - oh/FedBABU)] |
- [PUB - robust-noniid-optimizer)] |
- [PUB
- [PUB
- [PUB - RoD)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients)] |
- [PUB
- [PUB
- [PUB - research/federated/tree/master/optimization)] |
- [PUB - Non-IID)] |
- [PUB - air/FedBN)] |
- [PUB
- [PUB
- [PUB
- [PUB - disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix)] [[CODE](https://github.com/gdisag/gradient_disaggregation)] |
- [PUB - research/federated/tree/master/distributed_dp)] [[VIDEO](https://slideslive.com/38959306/the-distributed-discrete-gaussian-mechanism-for-federated-learning-with-secure-aggregation)] |
- [PUB - saligrama/Personalized-Federated-Learning)] [[VIDEO](https://slideslive.com/38959212/debiasing-model-updates-for-improving-personalized-federated-training)] |
- [PUB - Incentives)] [[VIDEO](https://slideslive.com/38959135/one-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning)] |
- [PUB - secure/CRFL)] [[VIDEO](https://slideslive.com/38959047/crfl-certifiably-robust-federated-learning-against-backdoor-attacks)] |
- [PUB - learning-under-arbitrary-communication-patterns)] |
- [PUB
- [PUB - One-bit-Distributed-Mean-Estimation)] |
- [PUB - Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning)] |
- [PUB - postech/gradient-inversion-generative-image-prior)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - xf-fan/Byzantine-Federeated-RL)] |
- [PUB
- [PUB
- [PUB - research/federated/tree/master/distributed_dp)] |
- [PUB
- [PUB
- [PUB - SysML/GradAttack)] |
- [PUB
- [PUB - Private-Federated-Bayesian-Optimization)] |
- [PUB - pFL)] |
- [PUB - research/federated/tree/master/reconstruction)] [[UC.](https://github.com/KarhouTam/FedRecon)] |
- [PUB
- [PUB - WBC)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - research/federated/tree/f4e26c1b9b47ac320e520a8b9943ea2c5324b8c2/large_cohort)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Linear-Contextual-Bandits)] |
- [PUB
- [PUB - fedjax.algorithms.mime)] [[VIDEO](https://papertalk.org/papertalks/37564)] |
- [PUB
- [PUB
- [PUB - optimization/FedDR)] |
- [PUB
- [PUB - secure/DBA)] |
- [PUB
- [PUB
- [PUB
- [PUB - research/federated/tree/master/gans)] |
- [PUB
- [PUB - a-communicationefficient-algorithm-for-federated-learning?ref=speaker-16993-latest)] |
- [PUB - communicationefficient-federated-learning-with-sketching)] [[CODE](https://github.com/kiddyboots216/CommEfficient)] |
- [PUB - learning-with-only-positive-labels)] |
- [PUB - local-sgd-to-local-fixed-point-methods-for-federated-learning)] |
- [PUB - for-compressed-gradient-descent-in-distributed-optimization)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FedAvg)] |
- [PUB - zoo)] |
- [PUB
- [PUB - NeurIPS20)] [[VIDEO](https://youtu.be/K28zpAgg3HM)] |
- [PUB
- [PUB - learning-public-code/tree/master/codes/FedDF-code)] |
- [PUB - in-cross-silo-fl)] |
- [PUB - federated-neural-matching)] |
- [PUB - group/ModelPoisoning)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - AI/FedML/tree/master/fedml_experiments/distributed/fedgkt)] [[解读](https://zhuanlan.zhihu.com/p/536901871)] |
- [PUB
- [PUB - Distributed-Learning-Tight-Error-Bounds-and-Breakdown-Point-under-Data-Heterogeneity)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - partial-participation)] |
- [PUB
- [PUB
- [PUB - projunit)] |
- [PUB
- [PUB - influence-function)] |
- [PUB
- [PUB - Proj-Spatial)] |
- [PUB - merging)] |
- [PUB - aketi/global_update_tracking)] |
- [PUB
- [PUB - mlg/fast-rec-with-grc)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - 2022/Slides/16881.pdf)] [[解读](https://www.bilibili.com/read/cv17092678)] |
- [PUB - federated-learning-using-hypernetworks)] [[解读](https://zhuanlan.zhihu.com/p/431130945)] |
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - lab/FedDG_Benchmark)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - FCL-7D65)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - sight)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - 1010/FedHyper)] |
- [PUB
- [PUB
- [PUB
- [PUB - THU/VFLAIR)] |
- [PUB
- [PUB - Computing/VertiBench)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - stochastic-controlled-averaging-for-federated-learning)] [[UC.](https://github.com/ramshi236/Accelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks)] [[解读](https://zhuanlan.zhihu.com/p/538941775)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - Bayesian-Federated-Learning-Framework-with-Online-Laplace-Approximation)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
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- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [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
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [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
-
fl in top system conference and journal
- [PUB - ku/heteroswitch)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [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
- [PUB - eval-in-fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [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
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [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
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
-
federated learning framework
-
benchmark
-
table
- MindSpore Federated - ai/mindspore.svg?color=red)](https://github.com/mindspore-ai/mindspore/stargazers)<br /> | | HUAWEI | | | [[DOC](https://mindspore.cn/federated/docs/zh-CN/r1.6/index.html)] [[PAGE](https://mindspore.cn/federated)] |
-
-
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
- [PUB
- [PUB
- [PUB
- [PUB - dai/fednh)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - secure-aggregation-mitigating-multiround-privacy-leakage-in-federated-learning)] [[CODE](https://openreview.net/attachment?id=nVV6S2sb_UL&name=supplementary_material)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - minimax)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - flip-federated-backdoor-attack)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - 23-pfedrec)] |
- [PUB
- [PUB - attack-on-fl)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [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)] |
- [PUB
- [PUB
- [PUB
- [PUB - FL)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - When-Federated-Learning-Meets-Split-Learning)] |
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [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 - 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 - Computing/PrivML)] |
- [PUB - air/HarmoFL)] [[解读](https://zhuanlan.zhihu.com/p/472555067)] |
- [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 - 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
- [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 - Guo/FedACD)] |
- [PUB - Guo/FedACD)] |
- [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 - AI/SpreadGNN)] [[解读](https://zhuanlan.zhihu.com/p/429720860)] |
- [PUB - class-imbalance-in-federated-learning)] [[CODE](https://github.com/balanced-fl/Addressing-Class-Imbalance-FL)] [[解读](https://zhuanlan.zhihu.com/p/443009189)] |
-
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
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB
- [PUB - sw/f2l)] |
- [PUB
- [PUB
-
fl in top secure conference and journal
- [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)] |
-
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)] |
-
fl in top ir conference and journal
-
fl in top db conference and journal
- 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
- [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
- [PUB - websoft/FedChain)] |
- [PUB
- [PUB - Li/fl_auction)] |
-
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)
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- [PUB - infocom23.pdf)] [[CODE](https://github.com/TL-System/plato/tree/main/examples/knot)] |
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- [PUB - fed)] |
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- [PUB
- [PUB - TR.pdf)] [[CODE](https://github.com/Distributed-Learning-Networking-Group/FedMoS/)] |
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- [PUB
- [PUB - infocom23.pdf)] [[WEIBO](https://weibo.com/2174209470/MBt1Mofxv)] |
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- [PUB - System/plato/)] |
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- [PUB - zh/TrustFL)] |
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fl in top conference and journal other fields
-
fl datasets
-
benchmark
- 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)
- IEEE Communications Surveys & Tutorials 2020
- J. Heal. Informatics Res. 2021 - 020-00082-4)
- Ad Hoc Networks 2024 - Privacy-Computing-in-Metaverse)
-
-
fl graph datasets
-
benchmark
-
-
tutorials
-
benchmark
-
-
course
-
conference special tracks
-
secret sharing
-
-
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)*
-
-
citation
-
secret sharing
-
Programming Languages
Categories
fl in top-tier journal
1,856
fl in top ml conference and journal
847
fl on graph data and graph neural networks
324
fl in top ai conference and journal
294
fl on tabular data
282
fl in top cv conference and journal
169
fl in top system conference and journal
138
fl in top network conference and journal
81
fl in top db conference and journal
42
fl in top nlp conference and journal
42
fl in top secure conference and journal
36
workshops
31
fl in top dm conference and journal
27
fl datasets
24
tutorials
10
fl in top conference and journal other fields
8
course
5
journal special issues
4
federated learning framework
3
update log
3
fl in top ir conference and journal
3
conference special tracks
2
fl graph datasets
1
citation
1