Zero-Trust Federated Learning Architectures for Privacy-Preserving Cross-Institutional Medical Risk Scoring

Collaborative clinical risk scoring across multi-institutional healthcare networks is essential
for developing robust predictive models in cardiology, oncology, and intensive care.
However, strict data privacy mandates (e.g., HIPAA, GDPR) and severe cybersecurity
vulnerabilities prohibit centralizing sensitive electronic health records (EHR). While
standard Federated Learning (FL) allows distributed model training without raw data
aggregation, traditional FL paradigms remain susceptible to adversarial poisoning, gradient
leakage attacks, and unverified client node trust. This paper establishes a novel Zero-Trust
Federated Learning (ZT-FL) architecture engineered for privacy-preserving, cross
institutional medical risk scoring. Combining differential privacy (DP) noise injection,
homomorphic encryption (HE) parameter aggregation, and continuous micro-segmentation
attestation, our framework enforces strict zero-trust security bounds without compromising
predictive utility. Grounded in non-linear risk parameterization modeling and enterprise risk
management architectures, our model captures non-stationary clinical heterogeneity across
distributed hospital nodes. Evaluated on a multi-center dataset of 250,000 patient records
across 15 hospital networks between 2020 and 2025, our ZT-FL framework achieved an
Area Under the Receiver Operating Characteristic curve (AUROC) of 0.912 and an Area
Under the Precision-Recall Curve (AUPRC) of 0.885 under a strict differential privacy
budget (epsilon = 1.5), outperforming standalone local hospital models by 18.4% while
mitigating gradient inversion attacks with 99.9% efficacy.