mlsanityData to 7 Oct 2026

Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

arXiv 2403.00873 ↗First posted 1 Mar 2024cs.CR cs.LGcited by 5

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Abstract (arXiv)

Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 41 of its 185 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

41 of 185 references.

References · 41

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  5. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  6. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  7. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  8. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  9. 2018A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspectivecited by 111
  10. 2019Reliable Federated Learning for Mobile Networkscited by 92
  11. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  12. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
29 more
  1. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  2. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  3. 2021EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learningcited by 53
  4. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  5. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  6. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  7. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  8. 2023DoCoFL: Downlink Compression for Cross-Device Federated Learningcited by 27
  9. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  10. 2021Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computingcited by 18
  11. 2019Blockchain for Internet of Things: A Surveycited by 15
  12. 2022HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaversecited by 14
  13. 2022Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learningcited by 13
  14. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  15. 2018A Survey on the Security of Blockchain Systemscited by 12
  16. 2023A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extractioncited by 12
  17. 2019VeriSmart: A Highly Precise Safety Verifier for Ethereum Smart Contractscited by 11
  18. 2022BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Frameworkcited by 11
  19. 2020FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanismcited by 10
  20. 2021Secure and Efficient Federated Learning Through Layering and Sharding Blockchaincited by 10
  21. 2023Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systemscited by 10
  22. 2022Privacy Leakage of Adversarial Training Models in Federated Learning Systemscited by 7
  23. 2022SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discriminationcited by 7
  24. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  25. 2018Performance Analysis of the Raft Consensus Algorithm for Private Blockchainscited by 5
  26. 2021Smart Contract Security: a Practitioners' Perspectivecited by 3
  27. 2019Detecting Sybil Attacks using Proofs of Work and Location in VANETscited by 2
  28. 2020Utilizing Public Blockchains for the Sybil-Resistant Bootstrapping of Distributed Anonymity Servicescited by 1
  29. 2021A Tractable Probabilistic Approach to Analyze Sybil Attacks in Sharding-Based Blockchain Protocolscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 4.

5 papers.

  1. 2024SoK: Blockchain-Based Decentralized AI (DeAI)cited by 6
  2. 2026Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learningcited by 0
  3. 2026SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning Systemcited by 0
  4. 2026The Power of Backdoor Absorption in Community Trainingcited by 0
  5. 2026Secure Decentralized Federated Learning via Gossip and Virtual Votingcited by 0