AI In Healthcare: Federated Learning Architectures Across Hospitals

Abstract

This article examines the transformative potential of Federated Learning (FL) in healthcare settings, addressing the fundamental tension between leveraging large-scale data for artificial intelligence development and preserving patient privacy. As healthcare organizations face increasingly stringent regulatory requirements while seeking to harness AI capabilities, traditional centralized data aggregation approaches present significant privacy and compliance challenges. Federated Learning emerges as an innovative solution by enabling collaborative model development across multiple healthcare institutions without sharing sensitive patient data. This architecture allows hospitals to collectively train sophisticated AI systems while maintaining data locality and regulatory compliance. Through detailed examination of FL fundamentals, implementation strategies, practical applications in cancer detection, and current technical challenges, this article provides a comprehensive overview of privacy-preserving machine learning in healthcare. The article further explores regulatory compliance frameworks, ethical considerations, and future directions for this rapidly evolving field, offering valuable insights for healthcare institutions and professionals navigating the intersection of AI innovation and patient privacy protection in an increasingly data-driven healthcare landscape.