Driven by the exponential increase of healthcare data and the urgent need for privacy-preserving analytics, federated learning (FL) has become a transformational method in next-generation healthcare systems. FL lets collaborative model training across dispersed edge devices and businesses, therefore maintaining data locality and boosting security, unlike conventional machine learning models that need centralized data aggregation. The use of FL in healthcare environments is examined in this chapter along with emerging trends, advancements, and issues, including multi-modal data integration, differential privacy, federated transfer learning, cross-silo and cross-device settings, and real-time tailored medicine. Emphasizing present uses and expected developments, we examine how FL affects precise diagnostics, remote patient monitoring, drug development, and clinical decision support systems. Along with the combination of blockchain, Internet of Things (IoT), and edge computing to help system dependability, interoperability, scalability, and ethical compliance are stressed. This chapter also describes the integration of FL with advanced language models (LLM) and generative knowledge transformation algorithms in medicine. Finally, the regulatory framework, data management, and standardization required for the use of FL in healthcare will be discussed.