Authors - H.M.H.H. Gunarathne, K.A. Dilini Kulawansa Abstract - Federated Learning enables the collaborative development of AI models in healthcare while preserving patient data confidentiality, offering a promising solution to privacy, regulatory, and data transfer challenges. Unlike conventional centralized learning, FL transmits only model updates, including gradients or aggregated parameters, rather than raw data, thereby enabling multiple institutions to collaboratively train models while maintaining data confidentiality. This review outlines that FL ensures model accuracy and generalizability of the model in privacy-aware healthcare applications. It also discusses more privacy preservation methods that are implemented in combination with Federated Learning, including Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation, and blockchain-based systems, which help to increase security, trust, and transparency. The paper has also reviewed the existing studies in the key areas of healthcare such as disease diagnosis, medical im-aging, remote patient monitoring, predictive analytics and Electronic Health Record management. By demonstrating the potential of FL to enable scalable, secure, and privacy-preserving AI systems, this review provides insights into its transformative role in advancing intelligent, patient-centered healthcare solutions.
