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Federated Learning with Personalization Layers

arXiv 1912.00818 ↗First posted 2 Dec 2019cs.LG cs.DC stat.MLcited by 533

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Abstract (arXiv)

The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence, improving data privacy. This sharply deviates from traditional machine learning and necessitates the design of algorithms robust to various sources of heterogeneity. Specifically, statistical heterogeneity of data across user devices can severely degrade the performance of standard federated averaging for traditional machine learning applications like personalization with deep learning. This paper pro-posesFedPer, a base + personalization layer approach for federated training of deep feedforward neural networks, which can combat the ill-effects of statistical heterogeneity. We demonstrate effectiveness ofFedPerfor non-identical data partitions ofCIFARdatasetsand on a personalized image aesthetics dataset from Flickr.

Key facts. Cited by 533 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2019.

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Citing papers per year: 2020: 1; 2021: 55; 2022: 83; 2023: 95; 2024: 118; 2025: 102; 2026: 79.

533 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. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  7. 2021Personalized Federated Learning using Hypernetworkscited by 210
  8. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  9. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  10. 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 140
  11. 2021Federated Learning for Open Bankingcited by 138
  12. 2022Federated Learning with Partial Model Personalizationcited by 114
38 more
  1. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  2. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  3. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  4. 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
  5. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  6. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  7. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  8. 2021Personalized Federated Learning with Gaussian Processescited by 76
  9. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  10. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  11. 2022Tackling Data Heterogeneity in Federated Learning with Class Prototypescited by 66
  12. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  13. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  14. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 61
  15. 2021Personalized Federated Learning through Local Memorizationcited by 57
  16. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 55
  17. 2022Personalized Subgraph Federated Learningcited by 54
  18. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  19. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  20. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  21. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 45
  22. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 45
  23. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  24. 2022A Survey on Heterogeneous Federated Learningcited by 42
  25. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  26. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  27. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  28. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  29. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  30. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  31. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  32. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  33. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  34. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  35. 2024BadMerging: Backdoor Attacks Against Model Mergingcited by 32
  36. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  37. 2022On the Convergence of Clustered Federated Learningcited by 31
  38. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31

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