Telemedicine via Edge-Cloud Healthcare: A Federated Semi Supervised Learning Resource Recommendation Approach Towards Building Sustainable Framework

Telemedicine is a boon from modern technological advancements as medical attention is primary need for worldwide human community. Medical sustainability is under 13 SDGs set by United Nations (UN). Medical infrastructures in developing nations are facing an acute shortage of resources and enormous consumption of energy. Telemedicine framework can provide a reliable supporting ecosystem via IoT based communication channels between physical and virtual infrastructure such as Edge-cloud services. In this paper a protocol for sustainable telemedicine based on edge-cloud framework is discussed using federated semi-supervised learning termed as FedTEL. The framework extracts log record data attributes from independent telemedicine setup using federated learning principles, such that secure data training across local/edge telemedicine environment is achieved. The protocol framework extracts recommendations and scheduling of resources attached under the telemedicine framework. The technique has achieved outperforming accuracy of 91.32% on data recommendations, resource recommendations, scheduling of resources and recommendations of Electronic Health Records (EHR) case-studies on distributed cloud ecosystem.