IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

Lirui Bi, Tasiu Muazu () and Omaji Samuel ()
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Lirui Bi: School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Tasiu Muazu: College of Computer and Information, Hohai University, Nanjing 210098, China
Omaji Samuel: Department of Computer Science, Edo State University Uzairue, Iyamho 312101, Nigeria

Sustainability, 2022, vol. 15, issue 1, 1-25

Abstract: We propose a decentralized medical trust management system using blockchain-based federated learning for large-scale Internet of Things (IoT) systems. The proposed system enables health institutions to share data without revealing the privacy of data owners. Health institutions form coalitions and the leader of each coalition is elected based on the proposed proof-of-trust collaboration (PoTC) consensus protocol. The PoTC consensus protocol is based on a weight difference game where trust scores, trust consistency value, and trust deviation are factors used for evaluating nodes in the blockchain. The trust of a node is obtained either through direct trust or recommended trust evaluations. Each leader elects an aggregator who has the most credibility to manage the proposed federated learning system. The leaders become the federated clients as well as validators while the aggregator is the federated server. To ensure the decentralization of nodes, a consortium blockchain is employed. Extensive simulations are performed, which show that the proposed system not only demonstrates scalability and credibility without compromising the accuracy, convergence, and resilience properties against malicious attackers but also outperforms existing trust management systems. A security analysis is also conducted, which shows that the proposed system is robust against trust-related attacks.

Keywords: blockchain; machine learning; IoT; proof-of-trust collaboration; trust evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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