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Adaptive Personalized Federated Learning

arXiv 2003.13461 ↗First posted 30 Mar 2020cs.LG cs.DC stat.MLcited by 353

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Abstract (arXiv)

Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize. In this paper, we advocate an adaptive personalized federated learning (APFL) algorithm, where each client will train their local models while contributing to the global model. We derive the generalization bound of mixture of local and global models, and find the optimal mixing parameter. We also propose a communication-efficient optimization method to collaboratively learn the personalized models and analyze its convergence in both smooth strongly convex and nonconvex settings. The extensive experiments demonstrate the effectiveness of our personalization schema, as well as the correctness of established generalization theories.

Key facts. Cited by 353 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 2; 2021: 69; 2022: 84; 2023: 58; 2024: 59; 2025: 52; 2026: 29.

353 papers.

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  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  4. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  5. 2021A Field Guide to Federated Optimizationcited by 312
  6. 2021Personalized Federated Learning using Hypernetworkscited by 210
  7. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  8. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  9. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
  10. 2021Federated Learning for Open Bankingcited by 138
  11. 2022Federated Learning with Partial Model Personalizationcited by 114
  12. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
38 more
  1. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  2. 2021Distributionally Robust Federated Averagingcited by 104
  3. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  4. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  5. 2021Personalized Federated Learning with Gaussian Processescited by 76
  6. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  7. 2022Divergence-aware Federated Self-Supervised Learningcited by 71
  8. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  9. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  10. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  11. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 61
  12. 2021Personalized Federated Learning through Local Memorizationcited by 57
  13. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  14. 2023Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Modelscited by 49
  15. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  16. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  17. 2022A Survey on Heterogeneous Federated Learningcited by 42
  18. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 41
  19. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  20. 2021Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspectivecited by 38
  21. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  22. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  23. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  24. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  25. 2022Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspacescited by 36
  26. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  27. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  28. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  29. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  30. 2021QuPeD: Quantized Personalization via Distillation with Applications to Federated Learningcited by 32
  31. 2023An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learningcited by 32
  32. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31
  33. 2022Achieving Personalized Federated Learning with Sparse Local Modelscited by 30
  34. 2021Federated f-Differential Privacycited by 29
  35. 2022Personalized Federated Learning With Graphcited by 29
  36. 2021UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysiscited by 28
  37. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
  38. 2021A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularizationcited by 27

50 most cited of 353 All 353 (JSON)