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Federated Machine Learning: Concept and Applications

arXiv 1902.04885 ↗First posted 13 Feb 2019cs.AI cs.CR cs.LGcited by 412

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Abstract (arXiv)

Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.

Key facts. Cited by 412 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Feb 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 126; 2022: 121; 2023: 83; 2024: 46; 2025: 22; 2026: 14.

412 papers.

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  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  2. 2021Towards Personalized Federated Learningcited by 464
  3. 2021A Field Guide to Federated Optimizationcited by 315
  4. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  5. 2022Transformers in Medical Imaging: A Surveycited by 245
  6. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  7. 2021Trustworthy AI: From Principles to Practicescited by 207
  8. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  9. 2021Federated Learning for Open Bankingcited by 141
  10. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  11. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  12. 2021Federated Unlearning via Class-Discriminative Pruningcited by 115
38 more
  1. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  2. 2021Trustworthy AI: A Computational Perspectivecited by 93
  3. 2022FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learningcited by 91
  4. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  5. 2024A Survey of Resource-efficient LLM and Multimodal Foundation Modelscited by 86
  6. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  7. 2021Federated Quantum Machine Learningcited by 82
  8. 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
  9. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  10. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  11. 2022Variational Model Inversion Attackscited by 75
  12. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  13. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  14. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  15. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  16. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  17. 2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
  18. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  19. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  20. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  21. 2025AI Flow: Perspectives, Scenarios, and Approachescited by 44
  22. 2022A Survey on Heterogeneous Federated Learningcited by 42
  23. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  24. 2023Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearningcited by 40
  25. 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
  26. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  27. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  28. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 36
  29. 2023Federated Incremental Semantic Segmentationcited by 36
  30. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  31. 2021Federated Graph Learning -- A Position Papercited by 35
  32. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  33. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  34. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  35. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  36. 2022Federated Contrastive Learning for Volumetric Medical Image Segmentationcited by 33
  37. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  38. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32

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