Open Access

ARTICLE

A Decentralized Identity Framework for Secure Federated Learning in Healthcare

Samuel Acheme^*, Glory Nosawaru Edegbe

Department of Computer Science, Edo State University Iyamho, Auchi, 312102, Edo State, Nigeria

* Corresponding Author: Samuel Acheme. Email:

Journal of Cyber Security 2026, 8, 1-31. https://doi.org/10.32604/jcs.2026.073923

Received 28 September 2025; Accepted 01 December 2025; Issue published 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 (VCs) issued by a designated Regulator agent to create a lightweight, cryptographically secure identity management layer, ensuring that only trusted, authorized participants contribute to model aggregation. The framework is empirically evaluated against a severe model poisoning attack. Without the framework, the attack successfully compromised model reliability, reducing overall accuracy to 0.68 and malignant recall to 0.51. With the proposed DID-based framework, malicious clients were successfully excluded, and performance rebounded to near-baseline levels (accuracy = 0.95, malignant recall = 0.99). Also, this system achieves significantly improved integrity protection with minimal overhead: total trust overhead per FL round measured 75.5 ms, confirming its suitability for low-latency environments. Furthermore, this latency is approximately 39.7 times faster when compared to the consensus step alone of an optimized distributed ledger-based trust system, validating the framework as a scalable and highly efficient solution for next-generation FL security.

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Keywords

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