Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

This paper presents a federated deep learning approach for cardiovascular disease risk prediction that enables model training across distributed healthcare institutions without centralizing sensitive patient data. The method addresses privacy concerns critical to regulated healthcare environments by keeping data local while collaboratively improving predictive accuracy through a decentralized learning framework.

This paper presents a federated deep learning approach for cardiovascular disease risk prediction that enables model training across distributed healthcare institutions without centralizing sensitive patient data. The method addresses privacy concerns critical to regulated healthcare environments by keeping data local while collaboratively improving predictive accuracy through a decentralized learning framework.

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