Data privacy has become a significant problem in the healthcare sector due to the growing computerization of healthcare information and information-driven health studies. Sensitive patient data must be protected against breaches and illegal access since these situations might have serious ethical and legal repercussions. Data security and privacy are compromised by traditional AI-driven healthcare systems’ reliance on centralized data gathering, which compiles private health information into one central database for training machine learning (ML) models. This issue is addressed by Federated Learning (FL), which permits several healthcare institutions to collaborate to get knowledge from distributed content without exchanging it. The application of federated learning in healthcare clinical trial research, therapeutic customization, and disease prediction. But FL by itself has drawbacks including high communication costs, computational inefficiency, and security risks like model inversion attacks. To improve FL's security and efficiency, cloud and fog computing are essential. For aggregating FL model updates, cloud computing offers high processing power and large-scale storage, while fog computing makes real-time AI processing possible at the network edge, nearer to hospital networks, medical IoT devices, and mobile health apps. This chapter describes the most advanced methods currently in use for protecting patient privacy using federated learning.