mlsanityData to 7 Oct 2026

Trustworthy Federated Learning: A Survey

arXiv 2305.11537 ↗First posted 19 May 2023cs.AIcited by 4

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Abstract (arXiv)

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 57 of its 205 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

57 of 205 references.

References · 57

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  4. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  5. 2019Robust Aggregation for Federated Learningcited by 314
  6. 2021Trustworthy AI: From Principles to Practicescited by 206
  7. 2020The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencecited by 194
  8. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  9. 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
  10. 2020Explainable Deep Learning: A Field Guide for the Uninitiatedcited by 127
  11. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 98
  12. 2020COVID-CT-Dataset: A CT Scan Dataset about COVID-19cited by 93
38 more
  1. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  2. 2019Reliable Federated Learning for Mobile Networkscited by 92
  3. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  4. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 86
  5. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  6. 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
  7. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  8. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
  9. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 61
  10. 2021Provably Secure Federated Learning against Malicious Clientscited by 60
  11. 2021Towards Fairness-Aware Federated Learningcited by 57
  12. 2020BaFFLe: Backdoor detection via Feedback-based Federated Learningcited by 52
  13. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  14. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  15. 2020An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approachcited by 39
  16. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 38
  17. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  18. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 35
  19. 2020Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directionscited by 25
  20. 2022Privacy-Preserving Aggregation in Federated Learning: A Surveycited by 25
  21. 2021FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robotscited by 18
  22. 2020VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoTcited by 17
  23. 2022FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learningcited by 17
  24. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  25. 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
  26. 2022Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realizationcited by 16
  27. 2020Quality Inference in Federated Learning with Secure Aggregationcited by 15
  28. 2020An Exploratory Analysis on Users' Contributions in Federated Learningcited by 15
  29. 2020A Systematic Literature Review on Federated Learning: From A Model Quality Perspectivecited by 15
  30. 2022Trustworthy Federated Learning via Blockchaincited by 15
  31. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  32. 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
  33. 2020BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learningcited by 12
  34. 2022DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Settingcited by 12
  35. 2020A VCG-based Fair Incentive Mechanism for Federated Learningcited by 11
  36. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  37. 2023Towards Interpretable Federated Learningcited by 11
  38. 2021RobustFed: A Truth Inference Approach for Robust Federated Learningcited by 9

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learningcited by 9
  2. 2025FedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Modelscited by 1
  3. 2025Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Modelscited by 0
  4. 2026Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Healthcited by 0