mlsanityData to 7 Oct 2026

A Systematic Survey of Blockchained Federated Learning

arXiv 2110.02182 ↗First posted 5 Oct 2021cs.CR cs.AIcited by 15

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Abstract (arXiv)

With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 39 of its 90 references that are in the map. First posted on arXiv 5 Oct 2021.

Builds on

39 of 90 references.

References · 39

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2018How To Backdoor Federated Learningcited by 745
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  11. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  12. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
27 more
  1. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  2. 2018Federated Learning via Over-the-Air Computationcited by 215
  3. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  4. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  5. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  6. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  7. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  8. 2018Blockchained On-Device Federated Learningcited by 105
  9. 2019Reliable Federated Learning for Mobile Networkscited by 92
  10. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  11. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  12. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  13. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  14. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  15. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  16. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  17. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  18. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  19. 2020BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecturecited by 20
  20. 2020Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Servicescited by 18
  21. 2020BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learningcited by 12
  22. 2019A blockchain-orchestrated Federated Learning architecture for healthcare consortiacited by 8
  23. 2020Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Frameworkcited by 7
  24. 2020Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approachcited by 4
  25. 2019Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharingcited by 3
  26. 2021Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approachcited by 3
  27. 2020Blockchain-based Federated Learning for Device Failure Detection in Industrial IoTcited by 2

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Cited by year

Citing papers per year: 2022: 7; 2023: 4; 2024: 3; 2025: 1.

15 papers.

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  1. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  2. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  3. 2022Trustworthy Federated Learning via Blockchaincited by 15
  4. 2022Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learningcited by 13
  5. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  6. 2022FedToken: Tokenized Incentives for Data Contribution in Federated Learningcited by 9
  7. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  8. 2024SoK: Blockchain-Based Decentralized AI (DeAI)cited by 6
  9. 2023Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verificationcited by 4
  10. 2025VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learningcited by 4
  11. 2023Federated Learning for Metaverse: A Surveycited by 3
  12. 2022FLock: Defending Malicious Behaviors in Federated Learning with Blockchaincited by 1
3 more
  1. 2023A Survey on Blockchain-Based Federated Learning and Data Privacycited by 1
  2. 2022A Framework for Verifiable and Auditable Federated Anomaly Detectioncited by 0
  3. 2023DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLTcited by 0