Federated Learning (FL) offers a novel method for training models collaboratively across multiple distributed data sources without the need to collect data in a central location. For healthcare organizations, where data protection and security are of utmost importance, FL offers a promising solution for utilizing insights from diverse datasets. At the same time, it ensures the preservation of patient confidentiality. This paper examines the difficulties and recent developments in applying FL to smart healthcare. We propose a framework that addresses technical and non-technical challenges, by utilizing privacy-preserving techniques, personalized learning algorithms, efficient communication protocols, and infrastructure support. The framework, based on existing literature and practical implementations, provides a complete roadmap for implementing FL in healthcare service while safeguarding patient privacy.