Remote Health Monitoring (RHM) use cases produce large amounts of sensitive data from a variety of sources, requiring effective storage, management, and analysis to extract valuable insights. While the cloud is often used to store and analyse substantial health data, directly connecting diverse medical devices to the cloud can be inefficient, negatively impact performance, and pose data privacy risks. Edge computing provides a solution by enabling devices to process data at the network’s edge, either through a nearby local data center or directly on the device itself. This approach satisfies the low-latency and data security needs of healthcare applications. Despite these advantages, edge computing faces limitations in the amount of data it can handle, and training machine learning (ML) algorithms solely on local data can reduce model effectiveness. Federated learning has emerged as a promising solution to these challenges. This chapter includes a comprehensive review of Federated learning applications for RHM to evaluate how well these solutions address the limitations of edge computing. A systematic search was conducted across databases such as Arxiv, IEEE, Medrxiv, and ScienceDirect, focusing on Federated Learning, Edge Computing, and Remote Health Monitoring. Out of 5,015 references identified, 233 papers were selected for full-text analysis after removing duplicates and irrelevant entries. Ultimately, 87 relevant studies were included in our analysis. The review indicates that federated edge solutions are being developed to overcome edge computing constraints. However, these solutions require further exploration, particularly concerning security, as they are not yet fully ready for widespread adoption. Federated learning remains in its early implementation phase and finding solutions to these challenges.