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Federated Evaluation of On-device Personalization

arXiv 1910.10252 ↗First posted 22 Oct 2019cs.LG stat.MLcited by 147

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Abstract (arXiv)

Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers. In this work, we describe methods to extend the federation framework to evaluate strategies for personalization of global models. We present tools to analyze the effects of personalization and evaluate conditions under which personalization yields desirable models. We report on our experiments personalizing a language model for a virtual keyboard for smartphones with a population of tens of millions of users. We show that a significant fraction of users benefit from personalization.

Key facts. Cited by 147 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 2; 2021: 37; 2022: 39; 2023: 30; 2024: 21; 2025: 10; 2026: 8.

147 papers.

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  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  2. 2021Towards Personalized Federated Learningcited by 464
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  4. 2021A Field Guide to Federated Optimizationcited by 315
  5. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  6. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  7. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  8. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  9. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 69
  10. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  11. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  12. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 56
38 more
  1. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  2. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  3. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 42
  4. 2022A Survey on Heterogeneous Federated Learningcited by 42
  5. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  6. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  7. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  8. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  9. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  10. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  11. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  12. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  13. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  14. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31
  15. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  16. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
  17. 2022Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferringcited by 28
  18. 2021Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneitycited by 27
  19. 2024Harmonizing Generalization and Personalization in Federated Prompt Learningcited by 27
  20. 2021Decentralized federated learning of deep neural networks on non-iid datacited by 25
  21. 2023The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillationcited by 25
  22. 2020Specialized federated learning using a mixture of expertscited by 23
  23. 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
  24. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  25. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  26. 2023Adaptive Test-Time Personalization for Federated Learningcited by 23
  27. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 22
  28. 2020PFL-MoE: Personalized Federated Learning Based on Mixture of Expertscited by 21
  29. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  30. 2022Proportional Fairness in Federated Learningcited by 21
  31. 2022SphereFed: Hyperspherical Federated Learningcited by 19
  32. 2021PFA: Privacy-preserving Federated Adaptation for Effective Model Personalizationcited by 18
  33. 2022Self-Aware Personalized Federated Learningcited by 16
  34. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  35. 2023PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guaranteescited by 15
  36. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
  37. 2021SPIDER: Searching Personalized Neural Architecture for Federated Learningcited by 13
  38. 2022PGFed: Personalize Each Client's Global Objective for Federated Learningcited by 13

50 most cited of 147 All 147 (JSON)