2022· International Journal of Data Engineering and Intelligent Computing· Vol 5, pp. 01-12· 0 citations
TL;DR
This paper provides a comprehensive survey of the intersection of FL, Blockchain, and Edge Computing, analyzing key opportunities, current solutions, and open challenges and discusses architectural frameworks, real-world applications, and future research directions.
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.
It is concluded that integrating FL with cross-chain blockchain technology can significantly enhance privacy, security, and collaboration among diverse AI systems, paving the way for next-generation decentralized intelligence.
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Federated Learning (FL) enables distributed machine learning without sharing raw data, but its reliance on a central aggregation server introduces critical vulnerabilities gradient inversion attacks, Byzantine poisoning, free-riding by rational participants, and single-point-of-failure risk. Blockchain has been propose...
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