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Towards Federated Learning at Scale: System Design

arXiv 1902.01046 ↗First posted 4 Feb 2019cs.LG cs.DC stat.MLcited by 832

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Abstract (arXiv)

Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.

Key facts. Cited by 832 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Feb 2019.

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Citing papers per year: 2021: 219; 2022: 172; 2023: 155; 2024: 132; 2025: 93; 2026: 61.

832 papers.

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  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  3. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 326
  4. 2021A Field Guide to Federated Optimizationcited by 315
  5. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  6. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  7. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  8. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  9. 2021Practical and Private (Deep) Learning without Sampling or Shufflingcited by 170
  10. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  11. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  12. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
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  1. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  2. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 134
  3. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  4. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  5. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  6. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  7. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
  8. 2021Adversarial Examples Make Strong Poisonscited by 119
  9. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  10. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  11. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 98
  12. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  13. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  14. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 90
  15. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  16. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  17. 2021Federated Learning: Opportunities and Challengescited by 84
  18. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
  19. 2021Federated Quantum Machine Learningcited by 82
  20. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 81
  21. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
  22. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 80
  23. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  24. 2022Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learningcited by 79
  25. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  26. 2022Variational Model Inversion Attackscited by 75
  27. 2022When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methodscited by 75
  28. 2021On Large-Cohort Training for Federated Learningcited by 74
  29. 2022Fishing for User Data in Large-Batch Federated Learning via Gradient Magnificationcited by 73
  30. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  31. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 67
  32. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  33. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  34. 2023Federated Learning of Gboard Language Models with Differential Privacycited by 66
  35. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  36. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  37. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  38. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 61

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