Privacy-preserving federated learning for multi-institutional healthcare systems

This article explores a federated learning framework designed for privacy-preserving collaboration across healthcare institutions without exposing sensitive patient data. The system integrates differential privacy, secure aggregation, and adaptive model personalization to ensure high model performance while maintaining regulatory compliance with HIPAA and GDPR. The architecture features client nodes at participating hospitals, a coordinator server for aggregating encrypted updates, and robust communication protocols. Technical innovations include FedAlign for schema harmonization, personalized federated learning for data heterogeneity, and gradient sanitization for preventing information leakage. Evaluation across applications including sepsis prediction, mammogram analysis, and COVID-19 diagnosis demonstrates significant improvements in generalizability and accuracy while addressing healthcare equity considerations and enabling broader AI adoption across resource-variable settings.