Federated Learning for Privacy-Preserving Healthcare Analytics

Healthcare data is inherently sensitive, making traditional centralized machine learning approaches problematic. This paper proposes a federated learning framework specifically designed for healthcare analytics that preserves patient privacy while enabling multi-institutional collaboration. We demonstrate our approach on three clinical datasets covering radiology, pathology, and electronic health records. Results show that federated models achieve comparable performance to centralized approaches while maintaining strict data privacy guarantees.