mlsanityData to 14 Oct 2026

A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy

arXiv 2302.10637 ↗First posted 21 Feb 2023cs.LG cs.CRcited by 24

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Abstract (arXiv)

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 147 of its 275 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

147 of 275 references.

References · 147

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  6. 2018Federated Learning with Non-IID Datacited by 1,108
  7. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  8. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,055
  9. 2020Adaptive Federated Optimizationcited by 1,046
  10. 2019Deep Leakage from Gradientscited by 1,021
  11. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  12. 2017A Survey on Multi-Task Learningcited by 840
38 more
  1. 2019Towards Federated Learning at Scale: System Designcited by 832
  2. 2018LEAF: A Benchmark for Federated Settingscited by 792
  3. 2018How To Backdoor Federated Learningcited by 765
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  6. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  9. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  10. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  11. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  12. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  13. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  14. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  15. 2021Towards Personalized Federated Learningcited by 464
  16. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  17. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  18. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  19. 2020iDLG: Improved Deep Leakage from Gradientscited by 382
  20. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  21. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  22. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  23. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  24. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  25. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  26. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 326
  27. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  28. 2019Robust Aggregation for Federated Learningcited by 317
  29. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  30. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
  31. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  32. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  33. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
  34. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  35. 2019Can You Really Backdoor Federated Learning?cited by 255
  36. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
  37. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  38. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 8; 2024: 9; 2025: 5; 2026: 2.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  3. 2024Recent Advances of Multimodal Continual Learning: A Comprehensive Surveycited by 39
  4. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  5. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  6. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  7. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  8. 2024SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPCcited by 9
  9. 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
  10. 2025FedSV: Byzantine-Robust Federated Learning via Shapley Valuecited by 6
  11. 2023StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participationcited by 4
  12. 2024Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimizationcited by 2
12 more
  1. 2025FuSeFL: Fully Secure and Scalable Federated Learningcited by 2
  2. 2023FedHGN: A Federated Framework for Heterogeneous Graph Neural Networkscited by 1
  3. 2024TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logiccited by 1
  4. 2025On the Fragility of Contribution Score Computation in Federated Learningcited by 1
  5. 2023Trustworthy Large Models in Vision: A Surveycited by 0
  6. 2023Horizontal Federated Computer Visioncited by 0
  7. 2024A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Modelcited by 0
  8. 2024Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimizationcited by 0
  9. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  10. 2025Power to the Clients: Federated Learning in a Dictatorship Settingcited by 0
  11. 2026RIFLE: Robust Distillation-based FL for Deep Model Deployment on Resource-Constrained IoT Networkscited by 0
  12. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0