mlsanityData to 7 Oct 2026

Federated Reconstruction: Partially Local Federated Learning

arXiv 2102.03448 ↗First posted 5 Feb 2021cs.LG cs.DCcited by 78

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Abstract (arXiv)

Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity. Approaches that require clients to communicate all model parameters can be undesirable due to privacy and communication constraints. Other approaches require always-available or stateful clients, impractical in large-scale cross-device settings. We introduce Federated Reconstruction, the first model-agnostic framework for partially local federated learning suitable for training and inference at scale. We motivate the framework via a connection to model-agnostic meta learning, empirically demonstrate its performance over existing approaches for collaborative filtering and next word prediction, and release an open-source library for evaluating approaches in this setting. We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 38 of its 58 references that are in the map. First posted on arXiv 5 Feb 2021.

Builds on

38 of 58 references.

References · 38

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Deep Learning with Differential Privacycited by 2,682
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2020Adaptive Federated Optimizationcited by 1,036
  8. 2019Deep Leakage from Gradientscited by 1,004
  9. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  12. 2017Federated Multi-Task Learningcited by 669
26 more
  1. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  2. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  3. 2019Federated Learning with Personalization Layerscited by 533
  4. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  5. 2020Adaptive Personalized Federated Learningcited by 353
  6. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  7. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  8. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  9. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  10. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  11. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  12. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  13. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  14. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  15. 2019Private Stochastic Convex Optimization with Optimal Ratescited by 183
  16. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  17. 2019Federated Evaluation of On-device Personalizationcited by 147
  18. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  19. 2019Secure Federated Matrix Factorizationcited by 102
  20. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  21. 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 63
  22. 2019Meta Matrix Factorization for Federated Rating Predictionscited by 48
  23. 2020Federated Multi-view Matrix Factorization for Personalized Recommendationscited by 27
  24. 2020FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learningcited by 24
  25. 2020Privacy Threats Against Federated Matrix Factorizationcited by 6
  26. 2016Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analyticscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 22; 2023: 29; 2024: 11; 2025: 10; 2026: 1.

78 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
  3. 2022Federated Learning with Partial Model Personalizationcited by 114
  4. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  5. 2022Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streamscited by 59
  6. 2022Federated Learning via Inexact ADMMcited by 44
  7. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  8. 2022A Survey on Heterogeneous Federated Learningcited by 42
  9. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  10. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  11. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  12. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
38 more
  1. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  2. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  3. 2022A Survey on Federated Recommendation Systemscited by 25
  4. 2024Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metricscited by 25
  5. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  6. 2022Learning to Generate Image Embeddings with User-level Differential Privacycited by 23
  7. 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learningcited by 23
  8. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
  9. 2023Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learningcited by 20
  10. 2022SphereFed: Hyperspherical Federated Learningcited by 19
  11. 2023Dual Personalization on Federated Recommendationcited by 19
  12. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  13. 2022Distributed Distributionally Robust Optimization with Non-Convex Objectivescited by 14
  14. 2022Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooringcited by 14
  15. 2021Efficient and Private Federated Learning with Partially Trainable Networkscited by 13
  16. 2023Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learningcited by 13
  17. 2023OPORP: One Permutation + One Random Projectioncited by 11
  18. 2023Federated Recommendation with Additive Personalizationcited by 9
  19. 2023pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learningcited by 9
  20. 2023Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuningcited by 9
  21. 2024Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imagingcited by 9
  22. 2024Co-clustering for Federated Recommender Systemcited by 9
  23. 2022Mixed Federated Learning: Joint Decentralized and Centralized Learningcited by 8
  24. 2023Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneitycited by 8
  25. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6
  26. 2023Private Learning with Public Featurescited by 6
  27. 2022Federated Fuzzy Neural Network with Evolutionary Rule Learningcited by 5
  28. 2023Personalised Federated Learning On Heterogeneous Feature Spacescited by 5
  29. 2023Differential Privacy with Random Projections and Sign Random Projectionscited by 5
  30. 2023A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Designcited by 5
  31. 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
  32. 2022Personalized Federated Learning with Exact Stochastic Gradient Descentcited by 4
  33. 2022FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillationcited by 4
  34. 2023When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMAcited by 4
  35. 2023Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysiscited by 4
  36. 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMMcited by 3
  37. 2023FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systemscited by 3
  38. 2023Now It Sounds Like You: Learning Personalized Vocabulary On Devicecited by 2

50 most cited of 78 All 78 (JSON)