Federated Learning and Blockchain for Secure Edge Computing: Opportunities and Challenges

  • Authors

    • Dr. Lakshmi Narayanan Associate Professor, Bharathidasan University, India. Author
    • Alan Turing Mathematician & Computer Scientist, University of Manchester, United Kingdom. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2022PI2D4M

    Published 02-13-2022

  • Federated Learning, Blockchain, Edge Computing, Privacy-Preserving Machine Learning, Decentralized AI, Consensus Mechanisms, Smart Contracts, Distributed Systems, Data Security, Resource-Constrained Devices

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    L. Narayanan and A. Turing, “Federated Learning and Blockchain for Secure Edge Computing: Opportunities and Challenges”, IJDEIC, vol. 5, no. 1, pp. 01–12, Feb. 2022, doi: 10.67228/30715717/IJDEIC-2022PI2D4M.

  • Abstract

    The convergence of Federated Learning (FL), Blockchain, and Edge Computing presents a transformative paradigm for decentralized, secure, and privacy-preserving machine learning at the network edge. FL enables collaborative model training without centralizing data, while Blockchain provides immutable and transparent mechanisms for trust, accountability, and coordination among distributed edge nodes. Edge computing further enhances this ecosystem by offering low-latency computation near data sources. Despite the promise of this triad, significant challenges persist in terms of scalability, energy efficiency, consensus mechanisms, data and model security, and system heterogeneity. This paper provides a comprehensive survey of the intersection of FL, Blockchain, and Edge Computing, analyzing key opportunities, current solutions, and open challenges. We also discuss architectural frameworks, real-world applications, and future research directions.

  • References

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