6G enabled federated learning for secure IoMT resource recommendation and propagation analysis

Abstract

The addition of devices in the IoT framework has leveraged the performance and ability of medical device computation in developing the Internet of Medical Things (IoMT) framework. The 6G ecosystem needs to be redefined in comparison with earlier generation communication environment and setup. This article proposes a novel technique of resource recommendation and scheduling-based propagation analysis usingFederated Learning (FL). The technique is driven by user-log record analysis and extraction of similar patterns. The logs are further evaluated with an available spectrum and bandwidth ratio of 6G to compute the requirement and availability of resources. The aim of the federated learning model is based on resource patterns and resource event occurrence fetched from computational logs of user participation. The framework uses the resource-attribute optimization technique for customization. The recommendation is based on strategic evaluation and the Dynamic User Allocation (DUA) technique. The approach has integrated data evaluation from the Operating system, networking channel, and communication devices of IoMT and has compared performance over the standard resource recommendation model. The proposed technique has achieved 94.72% of accuracy over standard DUA datasets.