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The rise of artificial intelligence (AI) in healthcare has created opportunities for advanced predictive models and personalized treatments, yet the sensitive nature of medical data presents significant challenges in terms of privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a promising solution to these issues, enabling decentralized machine learning across distributed datasets while preserving data privacy. This paper explores the application of FL in the healthcare domain, highlighting its potential to unlock valuable medical insights without the need for centralized data aggregation. We examine the technical architecture of federated learning, its privacy-preserving mechanisms such as differential privacy and secure multiparty computation, and the challenges of ensuring model accuracy and generalizability across diverse healthcare settings. Key case studies are reviewed to illustrate the practical benefits of FL in clinical data analysis, disease prediction, and personalized medicine. Additionally, this paper addresses current limitations; including communication overhead, model heterogeneity, and regulatory barriers, while proposing future directions for enhancing the scalability and adoption of federated learning in healthcare systems. By fostering collaborative intelligence without compromising data confidentiality, federated learning represents a critical step towards more secure, efficient, and equitable healthcare solutions. Keywords: Federated Learning (FL), Healthcare, Decentralized Machine Learning.
Mishra et al. (Fri,) studied this question.