A Decentralized Identity Framework for Secure Federated Learning in Healthcare
Samuel Acheme*, Glory Nosawaru Edegbe
Journal of Cyber Security, Vol.8, pp. 1-31, 2026, DOI:10.32604/jcs.2026.073923
- 07 January 2026
Abstract Federated learning (FL) enables collaborative model training across decentralized datasets, thus maintaining the privacy of training data. However, FL remains vulnerable to malicious actors, posing significant risks in privacy-sensitive domains like healthcare. Previous machine learning trust frameworks, while promising, often rely on resource-intensive blockchain ledgers, introducing computational overhead and metadata leakage risks. To address these limitations, this study presents a novel Decentralized Identity (DID) framework for mutual authentication that establishes verifiable trust among participants in FL without dependence on centralized authorities or high-cost blockchain ledgers. The proposed system leverages Decentralized Identifiers (DIDs) and Verifiable Credentials… More >
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