Federated Learning (FL) and Healthcare 5.0 are putting forward a massive change as far as medical data utilization succeeds toward the goal of improving patient outcomes while holding onto sovereignty and privacy of the data. As derived from the contents of the chapter, Federated Learning is a decentralized machine learning approach that will draw together health institutions to train models without disclosing any confidential patient information. Imagine a smart, personalized, and connected health environment where artificial intelligence, internet of things, cloud computing, and robotics all work together. This is what Healthcare 5.0 envisions. Federated Learning brings along this idea, providing solutions to serious issues of security, interoperability, and real-time judgment. From the course of this chapter, one could expect a full-scale discussion on the status of artificial intelligence (AI) in the healthcare industry at present, its capability to help in treatment personalization, and the possible avenues opened for ensuring ethical, safe, and scalable use of AI. The fine-grain analyses are performed with the significant restrictions of data diversity, communication overhead, and legal boundaries. The study further explores some future possibilities, including explainable Federated Learning, amalgamation with blockchain technology, and worldwide collaboration in research. The chapter gives strategic perspectives on how to build resilient digital health systems while ensuring strong patient privacy and a patient-oriented framework and even illustrates how Federated Learning is integrated into that framework of healthcare 5.0.