mlsanityData to 7 Oct 2026

Federated Learning: Challenges, Methods, and Future Directions

arXiv 1908.07873 ↗First posted 21 Aug 2019cs.LG cs.DC stat.MLcited by 1,531

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Cited by 25 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.

Key facts. Cited by 1,531 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2019.

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Citing papers per year: 2020: 1; 2021: 296; 2022: 308; 2023: 340; 2024: 256; 2025: 217; 2026: 113.

1,531 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  3. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  4. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  5. 2021Towards Personalized Federated Learningcited by 462
  6. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  7. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  8. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  9. 2021A Field Guide to Federated Optimizationcited by 312
  10. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  11. 2024Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunitiescited by 247
  12. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
38 more
  1. 2021Personalized Federated Learning using Hypernetworkscited by 210
  2. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  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 189
  5. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
  6. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  7. 2024Improving LoRA in Privacy-preserving Federated Learningcited by 158
  8. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  9. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  10. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  11. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 122
  12. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  13. 2021Decentralized Federated Averagingcited by 114
  14. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  15. 2022The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retrainingcited by 109
  16. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  17. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  18. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  19. 2023Stochastic Controlled Averaging for Federated Learning with Communication Compressioncited by 100
  20. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  21. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  22. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  23. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  24. 2024Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resourcescited by 93
  25. 2023SLoRA: Federated Parameter Efficient Fine-Tuning of Language Modelscited by 92
  26. 2023Deep Model Fusion: A Surveycited by 89
  27. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  28. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  29. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  30. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  31. 2023Data Distillation: A Surveycited by 86
  32. 2024Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Surveycited by 86
  33. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  34. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  35. 2021Federated Learning: Opportunities and Challengescited by 83
  36. 2021A Study of Face Obfuscation in ImageNetcited by 83
  37. 2021Federated Quantum Machine Learningcited by 82
  38. 2024Selective Aggregation for Low-Rank Adaptation in Federated Learningcited by 80

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