mlsanityData to 7 Oct 2026

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning

arXiv 2403.19178 ↗First posted 28 Mar 2024cs.CR cs.AI cs.DCcited by 8

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Abstract (arXiv)

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learning (FL), as a distributed machine learning framework, enables participants to collaboratively train models while safeguarding data privacy by avoiding direct raw data exchange. Despite the growing interest in decentralized methods, their application in FL remains underexplored. This paper presents a thorough investigation into Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's security features and FL's privacy-preserving model training capabilities. First, we present the taxonomy of BCFL from three aspects, including decentralized, separate networks, and reputation-based architectures. Then, we summarize the general architecture of BCFL systems, providing a comprehensive perspective on FL architectures informed by blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT, and other privacy-sensitive areas. Finally, we identify future research directions of BCFL.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 58 of its 145 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

58 of 145 references.

References · 58

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38 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. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
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50 most cited of 58 All 58 (JSON)

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Cited by year

Citing papers per year: 2024: 4; 2025: 4.

8 papers.

  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  2. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  3. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  4. 2024Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Datacited by 6
  5. 2024Swarm Learning: A Survey of Concepts, Applications, and Trendscited by 2
  6. 2025Efficient Federated Learning with Heterogeneous Data and Adaptive Dropoutcited by 2
  7. 2025Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applicationscited by 0
  8. 2025Verifiable Split Learning via zk-SNARKscited by 0