Enhancing Privacy and Collaborative Efforts in Healthcare through Federated Machine Learning

Abstract

- Healthcare institutions create data from complementary electronic health records, medical images, laboratory measurements, genomic profiles, and connected devices. While aggregating this data can enhance machine-learning models, centralized collection increases exposure risks, conflicts with institutional data sovereignty, and is often constrained by legal, ethical, and operational challenges. Federated Machine Learning (FML) provides a collaborative framework where participating organizations can train a common model while keeping raw data stored locally. This paper presents a design-oriented review and a practical framework for privacy-aware, cross-silo FML in healthcare. The framework integrates local data governance, standardized preprocessing, federated optimization, secure aggregation, differential privacy, update validation, audit logging, and continuous clinical monitoring. It explicitly distinguishes data locality from formal privacy guarantees: while federated training minimizes raw data movement, model updates can still leak information or be subject to manipulation. An illustrative experiment involving five statistically heterogeneous synthetic hospitals evaluated centralized learning, isolated local learning, federated averaging, and clipped-and-noised federated averaging. The results indicate that beneficial collaboration is technically feasible, but privacy, fairness, interoperability, governance, and clinical validation must be engineered as integral parts of the joint lifecycle requirements rather than treated as consequences of federation alone.