mlsanityData to 7 Oct 2026

Towards Personalized Federated Learning

arXiv 2103.00710 ↗First posted 1 Mar 2021cs.LG cs.AI cs.DCcited by 462

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Abstract (arXiv)

In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest towards privacy-preserving AI. This has contributed to the popularity of Federated Learning (FL), the leading paradigm for the training of machine learning models on data silos in a privacy-preserving manner. In this survey, we explore the domain of Personalized FL (PFL) to address the fundamental challenges of FL on heterogeneous data, a universal characteristic inherent in all real-world datasets. We analyze the key motivations for PFL and present a unique taxonomy of PFL techniques categorized according to the key challenges and personalization strategies in PFL. We highlight their key ideas, challenges and opportunities and envision promising future trajectories of research towards new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches.

Key facts. Cited by 462 papers in the map (data through 7 Oct 2026). Builds on 76 of its 138 references that are in the map. First posted on arXiv 1 Mar 2021.

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76 of 138 references.

References · 76

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  5. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  6. 2019Advances and Open Problems in Federated Learningcited by 3,021
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  8. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
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  10. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  11. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  12. 2018Federated Learning with Non-IID Datacited by 1,099
38 more
  1. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  2. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  4. 2018LEAF: A Benchmark for Federated Settingscited by 780
  5. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  9. 2020Federated Learning with Matched Averagingcited by 564
  10. 2019Federated Learning with Personalization Layerscited by 533
  11. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  12. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  13. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  14. 2020Learning under Concept Drift: A Reviewcited by 470
  15. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  16. 2019Agnostic Federated Learningcited by 451
  17. 2019Federated Machine Learning: Concept and Applicationscited by 409
  18. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  19. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  20. 2015Return of Frustratingly Easy Domain Adaptationcited by 389
  21. 2019Fair Resource Allocation in Federated Learningcited by 389
  22. 2017Measuring Catastrophic Forgetting in Neural Networkscited by 359
  23. 2020Adaptive Personalized Federated Learningcited by 353
  24. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  25. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  26. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  27. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  28. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  29. 2018Distributed learning of deep neural network over multiple agentscited by 277
  30. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  31. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  32. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  33. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  34. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  35. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  36. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  37. 2020Threats to Federated Learning: A Surveycited by 171
  38. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 65; 2023: 111; 2024: 130; 2025: 96; 2026: 47.

462 papers.

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  1. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  2. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  3. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  4. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
  5. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  6. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  7. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  8. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
  9. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  10. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  11. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  12. 2021Personalized Federated Learning with Gaussian Processescited by 76
38 more
  1. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
  2. 2022Divergence-aware Federated Self-Supervised Learningcited by 71
  3. 2022Tackling Data Heterogeneity in Federated Learning with Class Prototypescited by 66
  4. 2021Towards Fairness-Aware Federated Learningcited by 57
  5. 2023Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytescited by 55
  6. 2024Dual-Personalizing Adapter for Federated Foundation Modelscited by 54
  7. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  8. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  9. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
  10. 2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscapecited by 46
  11. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  12. 2022A Survey on Heterogeneous Federated Learningcited by 42
  13. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  14. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 39
  15. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  16. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  17. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  18. 2023Momentum Benefits Non-IID Federated Learning Simply and Provablycited by 37
  19. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  20. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  21. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  22. 2021GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learningcited by 35
  23. 2022FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reductioncited by 35
  24. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  25. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  26. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  27. 2022On the Convergence of Clustered Federated Learningcited by 31
  28. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  29. 2022Personalized Federated Learning With Graphcited by 29
  30. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 28
  31. 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuningcited by 27
  32. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  33. 2024Harmonizing Generalization and Personalization in Federated Prompt Learningcited by 26
  34. 2023The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillationcited by 25
  35. 2023Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneitycited by 25
  36. 2023Federated Domain Generalization: A Surveycited by 25
  37. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  38. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24

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