mlsanityData to 14 Oct 2026

Advances and Open Problems in Federated Learning

arXiv 1912.04977 ↗First posted 10 Dec 2019cs.LG cs.CR stat.MLcited by 3,046

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Cited by 56 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Key facts. Cited by 3,046 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Dec 2019.

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Citing papers per year: 2020: 2; 2021: 489; 2022: 577; 2023: 589; 2024: 524; 2025: 495; 2026: 370.

3,046 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 766
  2. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  3. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  4. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  5. 2021Towards Personalized Federated Learningcited by 464
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  7. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  8. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  9. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  10. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  11. 2021Personalized Federated Learning using Hypernetworkscited by 212
  12. 2021Trustworthy AI: From Principles to Practicescited by 207
38 more
  1. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  2. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
  3. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  4. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  5. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  6. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
  7. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 185
  8. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  9. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  10. 2021Practical and Private (Deep) Learning without Sampling or Shufflingcited by 170
  11. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  12. 2024Improving LoRA in Privacy-preserving Federated Learningcited by 161
  13. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  14. 2023How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacycited by 153
  15. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
  16. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  17. 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 141
  18. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  19. 2022ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!cited by 134
  20. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 134
  21. 2023Prodigy: An Expeditiously Adaptive Parameter-Free Learnercited by 133
  22. 2021SPECTRE: Defending Against Backdoor Attacks Using Robust Statisticscited by 130
  23. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  24. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  25. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  26. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  27. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  28. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
  29. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  30. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  31. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  32. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  33. 2021Federated Unlearning via Class-Discriminative Pruningcited by 115
  34. 2021Decentralized Federated Averagingcited by 114
  35. 2021Federated Graph Classification over Non-IID Graphscited by 114
  36. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  37. 2022Federated Learning with Partial Model Personalizationcited by 114
  38. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113

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