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.

• →FedCVR framework enables secure multi-institutional collaboration for cardiovascular risk prediction without compromising patient privacy.
• →The system achieved strong performance metrics with F1-score of 0.84 and AUC of 0.96 using differential privacy protections.
• →Server-side momentum optimization acts as a temporal denoiser to maintain model accuracy under privacy constraints.
• →The framework was validated against real-world datasets including Framingham and Cleveland cardiovascular databases.
• →Study provides an engineering blueprint for privacy-preserving AI in healthcare applications.