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Threats to Federated Learning: A Survey

arXiv 2003.02133 ↗First posted 4 Mar 2020cs.CR cs.LG stat.MLcited by 171

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Abstract (arXiv)

With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Federated learning (FL) has recently emerged as a promising solution under this new reality. Existing FL protocol design has been shown to exhibit vulnerabilities which can be exploited by adversaries both within and without the system to compromise data privacy. It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation. Currently, there is no survey on this topic. In this paper, we bridge this important gap in FL literature. By providing a concise introduction to the concept of FL, and a unique taxonomy covering threat models and two major attacks on FL: 1) poisoning attacks and 2) inference attacks, this paper provides an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks, and discuss promising future research directions towards more robust privacy preservation in FL.

Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 43; 2022: 22; 2023: 39; 2024: 36; 2025: 19; 2026: 11.

171 papers.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2021Dataset Condensation with Distribution Matchingcited by 362
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  5. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  6. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  7. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  8. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  9. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  10. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  11. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  12. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
38 more
  1. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  2. 2021Towards Fairness-Aware Federated Learningcited by 57
  3. 2021Natural Language Understanding with Privacy-Preserving BERTcited by 54
  4. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  5. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
  6. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  7. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  8. 2021Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrixcited by 43
  9. 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Taskscited by 41
  10. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  11. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  12. 2022Gradient Obfuscation Gives a False Sense of Security in Federated Learningcited by 37
  13. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  14. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  15. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  16. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 33
  17. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  18. 2022Federated Contrastive Learning for Volumetric Medical Image Segmentationcited by 32
  19. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  20. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 28
  21. 2022TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernelscited by 26
  22. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  23. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  24. 2022Indiscriminate Data Poisoning Attacks on Neural Networkscited by 24
  25. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  26. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  27. 2023Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attackscited by 23
  28. 2020PFL-MoE: Personalized Federated Learning Based on Mixture of Expertscited by 21
  29. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  30. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 20
  31. 2021FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robotscited by 18
  32. 2021A Novel Attribute Reconstruction Attack in Federated Learningcited by 18
  33. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  34. 2024Text-Enhanced Data-free Approach for Federated Class-Incremental Learningcited by 16
  35. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  36. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  37. 2021FedLess: Secure and Scalable Federated Learning Using Serverless Computingcited by 13
  38. 2022Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learningcited by 13

50 most cited of 171 All 171 (JSON)