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Personalized Federated Learning with Moreau Envelopes

arXiv 2006.08848 ↗First posted 16 Jun 2020cs.LG cs.DC stat.MLcited by 612

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Abstract (arXiv)

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data. One challenge associated with FL is statistical diversity among clients, which restricts the global model from delivering good performance on each client's task. To address this, we propose an algorithm for personalized FL (pFedMe) using Moreau envelopes as clients' regularized loss functions, which help decouple personalized model optimization from the global model learning in a bi-level problem stylized for personalized FL. Theoretically, we show that pFedMe's convergence rate is state-of-the-art: achieving quadratic speedup for strongly convex and sublinear speedup of order 2/3 for smooth nonconvex objectives. Experimentally, we verify that pFedMe excels at empirical performance compared with the vanilla FedAvg and Per-FedAvg, a meta-learning based personalized FL algorithm.

Key facts. Cited by 612 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 88; 2022: 95; 2023: 123; 2024: 123; 2025: 102; 2026: 80.

612 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  3. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  4. 2021Towards Personalized Federated Learningcited by 462
  5. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  6. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  7. 2021A Field Guide to Federated Optimizationcited by 312
  8. 2021Personalized Federated Learning using Hypernetworkscited by 210
  9. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  10. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  11. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  12. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
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  1. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
  2. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
  3. 2021Federated Learning for Open Bankingcited by 138
  4. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  5. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 133
  6. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  7. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 121
  8. 2022Federated Learning with Partial Model Personalizationcited by 114
  9. 2021Federated Graph Classification over Non-IID Graphscited by 113
  10. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  11. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  12. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  13. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  14. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  15. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  16. 2024Selective Aggregation for Low-Rank Adaptation in Federated Learningcited by 80
  17. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  18. 2021FedCM: Federated Learning with Client-level Momentumcited by 77
  19. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  20. 2021Personalized Federated Learning with Gaussian Processescited by 76
  21. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  22. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  23. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  24. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  25. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  26. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  27. 2021Personalized Federated Learning through Local Memorizationcited by 57
  28. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 55
  29. 2021FedLab: A Flexible Federated Learning Frameworkcited by 54
  30. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  31. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
  32. 2024FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learningcited by 47
  33. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  34. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 45
  35. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 45
  36. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  37. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 41
  38. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40

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