mlsanityData to 7 Oct 2026

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

arXiv 2305.04513 ↗First posted 8 May 2023cs.LG cs.CRcited by 5

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Abstract (arXiv)

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data heterogeneity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 38 of its 186 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

38 of 186 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2024BlockFUL: Enabling Unlearning in Blockchained Federated Learningcited by 3
  2. 2024Exemplar-condensed Federated Class-incremental Learningcited by 2
  3. 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Thingscited by 1
  4. 2026Federated Learning and Class Imbalancescited by 0
  5. 2026Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Reviewcited by 0