Privacy-Preserving Federated Learning Framework for Cardiovascular Disease Risk Prediction under Non-IID Data
TL;DR
This work designs a privacy-compliant federated learning architecture to realize clinical heart disease risk prediction, and the FedAvg algorithm will be used to enable cross-institutional collaborative training without sharing raw patient data to provide a feasible privacy-preserving solution for cross-hospital clinical collaboration.
Abstract
With the rapid growth of digital healthcare data and increasing concerns over data security and regulatory compliance, the need for privacy-preserving collaborative learning has become more urgent than ever. Nowadays, Cardiovascular Disease (CVD) has become the leading cause of global mortality, while traditional centralized medical model training is facing problems like severe data privacy barriers and data island. This work designs a privacy-compliant federated learning architecture to realize clinical heart disease risk prediction, FedAvg algorithm will be used to enable cross-institutional collaborative training without sharing raw patient data. At the same time, the research will adopt K-Means based non-IID to simulate real-world medical data heterogeneity. Experimental results show that the proposed framework achieves competitive performance compared with centralized training, the optimal test accuracy is 0.8704, exceeding the result of conventional centralized training. Therefore, this framework can provide a feasible privacy-preserving solution for cross-hospital clinical collaboration and offer a practical approach for future distributed medical risk prediction.