Federated learning (FL) represents an innovative approach to addressing data-sharing challenges within the healthcare sector. With increasing demands for privacy, security, and regulatory compliance, centralized collection of patient data is becoming more problematic. FL allows healthcare institutions to develop machine learning (ML) models without the need to share raw data, thereby advancing personalized medicine, diagnostics, and patient monitoring. This paper explores the role of FL in healthcare, particularly in improving health outcomes using data from various sources such as electronic health records (EHR), medical imaging, wearable devices, and Internet of medical things (IoMT) systems. Additionally, technical challenges related to security, privacy, and client reliability are discussed, along with potential solutions such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation.