mlsanityData to 7 Oct 2026

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

arXiv 1907.09693 ↗First posted 23 Jul 2019cs.LG cs.CR cs.DBcited by 371

Not on the map. Why?

Abstract (arXiv)

Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To achieve smooth flow and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.

Key facts. Cited by 371 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 47; 2022: 47; 2023: 89; 2024: 94; 2025: 63; 2026: 31.

371 papers.

Sort:

By area:

  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  5. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
  6. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  7. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  8. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  9. 2022Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalizationcited by 97
  10. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  11. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  12. 2021Federated Quantum Machine Learningcited by 82
38 more
  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  3. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  4. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 65
  5. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  6. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  7. 2022Personalized Subgraph Federated Learningcited by 54
  8. 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learningcited by 54
  9. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  10. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 52
  11. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  12. 2021A Survey on Dialogue Summarization: Recent Advances and New Frontierscited by 51
  13. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  14. 2024FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learningcited by 47
  15. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  16. 2022A Survey on Heterogeneous Federated Learningcited by 42
  17. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  18. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  19. 2023Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearningcited by 39
  20. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  21. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  22. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  23. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  24. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  25. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 29
  26. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  27. 2021Benchmarking Differential Privacy and Federated Learning for BERT Modelscited by 28
  28. 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
  29. 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuningcited by 27
  30. 2022Meta Knowledge Condensation for Federated Learningcited by 26
  31. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  32. 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
  33. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  34. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  35. 2023Federated Learning for Computer Visioncited by 22
  36. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  37. 2021User-Level Label Leakage from Gradients in Federated Learningcited by 21
  38. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21

50 most cited of 371 All 371 (JSON)