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

This research proposes a robust framework for secure cardiovascular risk prediction, leveraging differentially private federated learning to ensure data privacy while enabling accurate medical insights, which is vital for real-world healthcare applications involving sensitive patient data.