mlsanityData to 7 Oct 2026

Privacy-Preserving in Blockchain-based Federated Learning Systems

arXiv 2401.03552 ↗First posted 7 Jan 2024cs.CR cs.AI cs.LGcited by 7

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Abstract (arXiv)

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 174 references that are in the map. First posted on arXiv 7 Jan 2024.

Builds on

31 of 174 references.

References · 31

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  5. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  7. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  8. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  9. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
  10. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  11. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  12. 2018Blockchained On-Device Federated Learningcited by 105
19 more
  1. 2019Reliable Federated Learning for Mobile Networkscited by 92
  2. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  3. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
  4. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  5. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  6. 2018Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchainscited by 48
  7. 2019Local Differential Privacy for Deep Learningcited by 46
  8. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  9. 2019Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithmcited by 26
  10. 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
  11. 2022Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networkscited by 15
  12. 2022Trustworthy Federated Learning via Blockchaincited by 15
  13. 2023Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshnesscited by 15
  14. 2020Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learningcited by 12
  15. 2023Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systemscited by 10
  16. 2022HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detectioncited by 5
  17. 2022An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportationcited by 2
  18. 2020Survey of Crosschain Communications Protocolscited by 1
  19. 2023A Survey on Blockchain-Based Federated Learning and Data Privacycited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  2. 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
  3. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  4. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  5. 2025GShield: Mitigating Poisoning Attacks in Federated Learningcited by 1
  6. 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Surveycited by 0
  7. 2026Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systemscited by 0