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Personalized Cross-Silo Federated Learning on Non-IID Data

arXiv 2007.03797 ↗First posted 7 Jul 2020cs.LG cs.DC stat.MLcited by 282

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Abstract (arXiv)

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.

Key facts. Cited by 282 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 36; 2022: 52; 2023: 52; 2024: 56; 2025: 58; 2026: 28.

282 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2021A Field Guide to Federated Optimizationcited by 312
  5. 2021Personalized Federated Learning using Hypernetworkscited by 210
  6. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  7. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  8. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 121
  9. 2021Federated Graph Classification over Non-IID Graphscited by 113
  10. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  11. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  12. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
38 more
  1. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  2. 2021Personalized Federated Learning with Gaussian Processescited by 76
  3. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  4. 2021Personalized Federated Learning through Local Memorizationcited by 57
  5. 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networkscited by 57
  6. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
  7. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  8. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  9. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  10. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 45
  11. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  12. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 45
  13. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  14. 2022A Survey on Heterogeneous Federated Learningcited by 42
  15. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  16. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  17. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  18. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  19. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  20. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  21. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  22. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Modelcited by 33
  23. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  24. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  25. 2022Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferringcited by 28
  26. 2022FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learningcited by 27
  27. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  28. 2022Meta Knowledge Condensation for Federated Learningcited by 26
  29. 2024Harmonizing Generalization and Personalization in Federated Prompt Learningcited by 26
  30. 2023The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillationcited by 25
  31. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  32. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  33. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  34. 2022FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregationcited by 21
  35. 2023FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learningcited by 21
  36. 2024Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchorscited by 21
  37. 2024Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Methodcited by 21
  38. 2021A Novel Attribute Reconstruction Attack in Federated Learningcited by 18

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