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

Abstract

Artificial intelligence (AI) is reshaping healthcare by advancing predictive analytics, medical imaging, drug discovery, and personalized treatment. Yet, progress is conAstrained by the need for large, diverse datasets and the challenges of privacy, regulation, and institutional data silos. Federated learning (FL) has emerged as a privacypreserving solution that trains models locally and shares parameters instead of raw data, enabling collaboration without compromising confidentiality. FL has been applied in cancer diagnosis, COVID-19 research, genomics, and electronic health records, showing strong potential for medical innovation. However, technical issues—such as data heterogeneity, communication overhead, model convergence, and security risks—along with economic and organizational barriers like cost, skills, and interoperability, slow adoption. Despite these challenges, FL is both a technological breakthrough and a strategic enabler for collaborative healthcare research. Hybrid approaches integrating FL with homomorphic encryption, differential privacy, and blockchain governance, combined with supportive policies and international cooperation, will be vital to realizing its full potential in healthcare.