Abstract: This paper provides an extensive review of federated learning (FL) applications and challenges in the healthcare domain, with emphasis on its transformative role in enabling collaborative learning while addressing critical privacy and security concerns. FL, as a decentralized machine-learning paradigm, allows multiple clients, such as hospitals or medical institutions, to collaboratively train models without sharing raw data. This renders FL particularly suitable for the healthcare sector, where sensitive patient information is governed by stringent privacy regulations and ethical considerations. This paper first introduces the fundamental concepts of FL, including its definition, architecture, and training process. How FL differs from conventional centralized learning by maintaining data localization while enabling a global model to benefit from diverse datasets is explained. This characteristic is particularly valuable in healthcare, where data are typically siloed across institutions and regions owing to legal and operational constraints. The applications of FL in healthcare are categorized into three primary areas: classification tasks, segmentation tasks, and other specialized use cases. In classification tasks, FL has been employed for disease-diagnosis models, such as for predicting diabetes risk, detecting Alzheimer’s disease, and identifying cancers. These applications demonstrate FL’s ability in leveraging distributed data for improving diagnostic accuracy. For segmentation tasks, FL is applied in medical-image analysis, including tumor-boundary delineation in MRI scans and lung-nodule detection in CT scans. Additionally, FL enables the integration and analysis of electronic health records (EHRs) across institutions, thus enhancing data utility while ensuring compliance with privacy standards. However, the deployment of FL in healthcare presents some challenges. One major issue is data heterogeneity, where variations in data distributions across institutions can adversely affect model performance and convergence. Privacy and security concerns remain significant, as FL must ensure the confidentiality of local data and the security of model updates during training and aggregation processes. Another critical challenge is the high communication cost, particularly in scenarios involving large-scale, multi-institutional collaborations. Frequent communication between clients and the central server or aggregators can introduce latency and increase resource demands. Hence, this study aims to identify innovative solutions and propose future research directions. Techniques such as personalized FL algorithms and transfer-learning approaches are proposed to address data heterogeneity. Privacy-preserving mechanisms, including differential privacy, secure multiparty computation, and homomorphic encryption, are highlighted to ensure robust data protection. Communication efficiency can be improved using advanced aggregation methods such as hierarchical FL and compression techniques. These innovations are expected to reduce communication overhead and enhance scalability in practical implementations. Results from existing studies indicate the effectiveness of FL in improving healthcare outcomes. For example, FL resulted in highly accurate multisite breast-cancer classification, improved disease-prediction models, and enabled the secure integration of EHR data. These advancements showcase FL’s potential in revolutionizing medical research and practice by fostering cross-institutional collaboration while maintaining patient confidentiality. In conclusion, this review emphasizes FL’s pivotal role in addressing critical challenges in healthcare data analysis and collaborative modeling. By leveraging its unique features and addressing its limitations, FL is well-positioned to propel significant advancements in disease diagnosis, personalized treatments, and large-scale medical research. This study serves as a foundation for future studies and advocates for continued innovation to satisfy the evolving demands of the healthcare industry.