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Lookup NU author(s): Dr Ahmad Alsharidah, Dr Dev JhaORCiD, Dr Ellis SolaimanORCiD, Dr Bo WeiORCiD, Dr Gagangeet Aujla, Professor Raj Ranjan
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.
Author(s): Alsharidah AA, Jha DN, Solaiman E, Wei B, Aujla GS, Ranjan R
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Consumer Electronics
Year: 2025
Pages: epub ahead of print
Online publication date: 27/10/2025
Acceptance date: 17/10/2025
Date deposited: 05/11/2025
ISSN (print): 0098-3063
ISSN (electronic): 1558-4127
Publisher: IEEE
URL: https://doi.org/10.1109/TCE.2025.3626199
DOI: 10.1109/TCE.2025.3626199
ePrints DOI: 10.57711/7waj-t972
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