Optimizing Edge Computing ForReal-Time Healthcare Monitoring Using Federated Learning

Real-time healthcare monitoring uses wearables and bedside sensors to watch patients' vital signs and alert caregivers quickly.Sending all data to the cloud can be slow and risky for privacy.Edge computing processes data close to where it is collected, which lowers delay and saves bandwidth.Federated learning lets many devices train a shared model without sending raw patient data, which supports privacy.This paper presents a simple, practical framework that combines edge computing and federated learning for faster, safer health monitoring.Our design chooses which devices should join each training round based on their battery, signal quality, and recent data.We reduce network load using light model updates with quantization and sparsification, and we add secure aggregation and differential privacy to protect patients' information.We also include small "personalization" parts in the model so each device can adapt to its patient.We describe a step-by-step method, an objective that balances accuracy, latency, and energy, and an evaluation plan using public physiological datasets under changing network conditions.Expected results show similar accuracy to standard training, with lower latency, fewer false alarms, and less bandwidth use.This work offers a clear path to deploy trustworthy, real-time monitoring at the edge.