This research introduces a new approach to healthcare data processing in real-time by combining Wireless Body Area Networks, federated learning, and edge computing. As major privacy issues, increased energy usage, and long processing times are associated with the conventional centralized data processing method. In order to reduce latency and energy consumption, this research proposes deploying edge computing to process data closer to the data source. Federated learning further improves privacy and security by allowing for collaborative model training across edge devices without exchanging raw data. We conduct an evaluation of the proposed integration of WBANs, federated learning, and edge computing using both theoretical and practical approaches.