A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

Researchers developed FedCVR, a privacy-preserving federated learning framework for cardiovascular risk prediction that enables secure collaboration across medical institutions. The system achieved an F1-score of 0.84 and AUC of 0.96 while maintaining differential privacy, demonstrating that server-side adaptive optimization can preserve clinical utility under strict privacy constraints.