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Survey of Personalization Techniques for Federated Learning

arXiv 2003.08673 ↗First posted 19 Mar 2020cs.LG stat.MLcited by 144

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Abstract (arXiv)

Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.

Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 37; 2022: 28; 2023: 32; 2024: 23; 2025: 17; 2026: 6.

144 papers.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  3. 2021Personalized Federated Learning using Hypernetworkscited by 210
  4. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  5. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  6. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  7. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  8. 2021Federated Quantum Machine Learningcited by 82
  9. 2021Personalized Federated Learning with Gaussian Processescited by 76
  10. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  11. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  12. 2021Federated Learning with Fair Averagingcited by 59
38 more
  1. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  2. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  3. 2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscapecited by 46
  4. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  5. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  6. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  7. 2021Device-Cloud Collaborative Learning for Recommendationcited by 34
  8. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  9. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  10. 2021UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysiscited by 28
  11. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 28
  12. 2021Federated Mixture of Expertscited by 27
  13. 2022TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernelscited by 26
  14. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  15. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  16. 2022FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networkscited by 23
  17. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22
  18. 2020PFL-MoE: Personalized Federated Learning Based on Mixture of Expertscited by 21
  19. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 21
  20. 2023Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learningcited by 20
  21. 2023AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environmentscited by 19
  22. 2022Federated Learning with Heterogeneous Architectures using Graph HyperNetworkscited by 17
  23. 2022Personalized PCA: Decoupling Shared and Unique Featurescited by 17
  24. 2023FedICT: Federated Multi-task Distillation for Multi-access Edge Computingcited by 17
  25. 2021FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognitioncited by 16
  26. 2024Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Modelscited by 16
  27. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  28. 2021Collaboration Equilibrium in Federated Learningcited by 15
  29. 2022PGFed: Personalize Each Client's Global Objective for Federated Learningcited by 13
  30. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  31. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  32. 2023Provably Personalized and Robust Federated Learningcited by 11
  33. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  34. 2021Communication-Efficient Agnostic Federated Averagingcited by 10
  35. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusioncited by 10
  36. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  37. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Datacited by 10
  38. 2021WAFFLE: Weighted Averaging for Personalized Federated Learningcited by 9

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