Privacy-Preserving Federated Learning for Intelligent Disease Diagnosis Across Distributed Healthcare Institutions

Abstract

The fragmentation of patient records across hospitals restricts the development of generalizable diagnostic models while data protection obligations discourage central data pooling. This paper develops a privacy-preserving federated learning framework for intelligent binary disease risk classification across distributed healthcare institutions. The proposed architecture combines institution-local preprocessing, a shared neural diagnostic classifier, weighted federated averaging, norm clipping, optional differential privacy, and secure aggregation. It explicitly addresses cross-site label imbalance, patient-level leakage, institution heterogeneity, and clinically meaningful evaluation. A detailed mathematical formulation and governance workflow specify how institutions can collaborate without transmitting identifiable patient records. Because no actual multicenter clinical dataset or training execution is available for this manuscript, the numerical results are illustrative scenario values rather than experimental evidence. The scenarios demonstrate how accuracy, sensitivity, communication cost, and privacy strength should be jointly examined before deployment. The work contributes a reproducible study protocol, clear privacy threat model, and clinically oriented reporting framework; external validation remains necessary before diagnostic use.