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Three Approaches for Personalization with Applications to Federated Learning

arXiv 2002.10619 ↗First posted 25 Feb 2020cs.LG stat.MLcited by 325

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Abstract (arXiv)

The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model per user. In this work, we present a systematic learning-theoretic study of personalization. We propose and analyze three approaches: user clustering, data interpolation, and model interpolation. For all three approaches, we provide learning-theoretic guarantees and efficient algorithms for which we also demonstrate the performance empirically. All of our algorithms are model-agnostic and work for any hypothesis class.

Key facts. Cited by 325 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 65; 2022: 79; 2023: 61; 2024: 62; 2025: 38; 2026: 19.

325 papers.

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  1. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  2. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  3. 2021Towards Personalized Federated Learningcited by 464
  4. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  5. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  6. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  7. 2021A Field Guide to Federated Optimizationcited by 315
  8. 2021Personalized Federated Learning using Hypernetworkscited by 212
  9. 2023Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Modelscited by 203
  10. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  11. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  12. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
38 more
  1. 2021Federated Learning for Open Bankingcited by 141
  2. 2022Federated Learning with Partial Model Personalizationcited by 114
  3. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  4. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  5. 2021Distributionally Robust Federated Averagingcited by 104
  6. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  7. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  8. 2023Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Modelscited by 85
  9. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  10. 2021Personalized Federated Learning with Gaussian Processescited by 76
  11. 2024RLHF from Heterogeneous Feedback via Personalization and Preference Aggregationcited by 74
  12. 2022Divergence-aware Federated Self-Supervised Learningcited by 73
  13. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 69
  14. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 63
  15. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  16. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  17. 2021Personalized Federated Learning through Local Memorizationcited by 57
  18. 2022Personalized Subgraph Federated Learningcited by 54
  19. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  20. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  21. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  22. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  23. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  24. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 42
  25. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  26. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  27. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  28. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  29. 2022Federated Learning under Distributed Concept Driftcited by 36
  30. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  31. 2022Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspacescited by 36
  32. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  33. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  34. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  35. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  36. 2021QuPeD: Quantized Personalization via Distillation with Applications to Federated Learningcited by 32
  37. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  38. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31

50 most cited of 325 All 325 (JSON)