QKD-Assisted Secure Transport Framework for Federated Healthcare Systems: A Software Prototype and Baseline Evaluation

Abstract

Decentralised machine learning requires authenticated transport to secure exchanged model updates. To this end, this study presents a healthcare-focused proof of concept integrating a quantum key distribution (QKD) simulation with HKDF-SHA-256, AES-256-GCM, role-based access control, replay detection, and sample-weighted federated averaging (FedAvg), and evaluates it on 42,989 medical records across 1107 synthetic patients. A trusted server decrypted authorised updates before plaintext aggregation. Centralised logistic regression achieved an F1 score of 0.7483, while a five-client FedAvg achieved 0.7333. Encrypted and unencrypted pipelines demonstrated identical model parameters. The simulated QKD protocol produced a mean quantum bit error rate of 0.2486 and successfully aborted all sessions under a full intercept constraint. Under ten independent data splits, the five-client FedAvg achieved mean F1 scores of 0.6973 (IID) and 0.6104 (non-IID). Demonstrating that transport security does not mitigate adversarial machine learning, an authenticated sign-flip attack bypassed AES-256-GCM protection and reduced the mean F1 score to 0.1185. Computationally, the QKD-assisted path averaged 0.386 ms per update compared to 0.486 ms for fresh TLS 1.3 and 0.006 ms for persistent TLS while isolating software execution from hardware constraints. Overall, this study demonstrates a reproducible proof of concept and a practical baseline for a secure machine learning approach for healthcare collaborations.