Federated Learning Framework for Consumer IoMT-Edge Resource Recommendation Under Telemedicine Services

Medical IoT devices and Telemedicine computation is the growing domain and further involving biomedical computation via machine learning ecosystem has generated an insightful results and analysis. The resources sharing and availability in computing and decision support suffer with a higher latency and energy consumption. In this manuscript, a novel TinyML based model for medical consumer devices resources allocation and resource sharing is discussed. The proposed framework is developed using Federated learning (FL) models for extracting the resource utilization patterns at individual user levels. These locally computed models are further facilitated with edge computation layer for locating resource patterns extraction. The technique is deployed on the dynamic server based resource pooling for effective analysis and resource scheduling and expanded to develop a reliable recommendation model for medical resource management. The framework has trained 128 clusters of 6400 rural and 12800 urban IoT devices samples for resource allocation and scheduling using telemedicine protocol (TelMED). The framework has secured an efficiency of 93.21% in urban user recommendation and 94.72% for rural users.