Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things

Gan, Chenquan; Xiao, Xinghai; Zhu, Qingyi; Jain, Deepak Kumar; Saini, Akanksha; Hussain, Amir

Abstract

In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low-quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL-driven dual-blockchain approach to address these challenges and improve data sharing and reputation management. Our approach comprises two blockchains: the Model Quality Blockchain (MQchain) and the Reputation Incentive Blockchain (RIchain). MQchain utilizes an enhanced Proof of Quality (PoQ) consensus algorithm to exclude low-quality nodes from participating in aggregation, effectively mitigating single points of failure and poisoning attacks by leveraging node reputation and quality thresholds. In parallel, RIchain incorporates a reputation evaluation, incentive mechanism, and index query mechanism, allowing for rapid and comprehensive node evaluation, thus identifying high-reputation nodes for MQchain. Security analysis confirms the theoretical soundness of the proposed method. Experimental evaluation using real medical datasets, specifically MedMNIST, demonstrates the remarkable resilience of our approach against attacks compared to three alternative methods.

Citation

Gan, C., Xiao, X., Zhu, Q., Jain, D. K., Saini, A., & Hussain, A. (online). Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things. Expert Systems, https://doi.org/10.1111/exsy.13714

• Journal Article Type: Article
• Acceptance Date: Aug 8, 2024
• Online Publication Date: Sep 5, 2024
• Deposit Date: Sep 17, 2024
• Journal: Expert Systems
• Print ISSN: 0266-4720
• Electronic ISSN: 1468-0394
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1111/exsy.13714
• Keywords: internet of medical things, data sharing, reputation management, federated learning, model-quality blockchain, reputation-incentive blockchain