Federated Learning in Healthcare: A Privacy-Preserving Approach to Collaborative Medical AI

Abstract

Federated learning (FL) has emerged as a revolutionary approach to training machine learning models across decentralized healthcare institutions while maintaining patient privacy. This paper explores the principles of FL, its applications in medical image analysis, patient risk prediction, and multi-institutional collaborations. It also addresses key challenges, including data heterogeneity, communication overhead, and adversarial attacks in FL frameworks. The study discusses strategies for improving model generalizability, security enhancements using differential privacy, and the future potential of federated learning in real-world clinical settings.