Medical image processing is crucial in modern healthcare by aiding diagnosis, treatment planning, and disease monitoring. However, practical analysis of medical images often necessitates large, diverse datasets, which pose significant privacy and data-sharing challenges. Federated learning (FL), a decentralized machine learning approach, enables the collaborative training of the model across multiple institutions without sharing sensitive patient data. This chapter comprehensively analyzes federated learning algorithms, techniques, and architectures specifically designed for healthcare applications. It examines the technological and infrastructural prerequisites and regulatory and ethical factors and presents various case examples to illustrate the practical implementation of these approaches. The chapter aims to correlate the application of federated learning and techniques to medical image processing, focusing on improving privacy and collaboration in healthcare settings. It also addresses typical constraints and obstacles associated with federated learning, including communication overhead, data heterogeneity, and algorithm convergence. The FL paradigm offers a solution to these problems by enabling algorithms to learn from diverse medical imaging datasets across several locations through collaborations facilitated by a central server. The findings indicate that federated learning holds considerable promise in transforming the healthcare sector by utilizing extensive medical data while upholding patient information confidentiality. Ongoing research and development are needed to harness its potential fully, ultimately resulting in enhanced patient care, medical research advancements, and increased healthcare system efficiency.