Deep reinforcement learning-based full-duplex link scheduling in federated learning-based computing for IoMT

The rapid developments in mini-hardware manufacturing and wireless network communications have enabled the Internet of Medical Things (IoMT) to provide continuous healthcare services over the Internet. Federated learning (FL) combined with blockchain technology has been a popular way to resolve privacy-preserving data sharing in IoMT-based wireless body area networks (WBANs), on the other side, communication payloads become much heavier than traditional healthcare sensor network, because central server should aggregate the model updates and orchestrate the training tasks. The high latency will lead to FL's low system efficiency. However, the existing studies on FL mainly focus on the system design and algorithm optimization, which ignore a critical problem of data transmission in the FL system. To improve the communication performance, we proposed a two-tier scheduling algorithm in which a full-duplex (FD) multiple access based scheduling algorithm is employed to improve channel utility and network throughput, and decrease the delay in tier II. A deep reinforcement learning (DRL) framework is used to generate the FD links between hubs and access points (APs) which jointly considers the channel state, fairness, and delay. Therefore, the DRL-based FD Link Scheduling (R-FDLS) algorithm is proposed. When the traffic volume is different or in various distribution scenarios, the evaluation results demonstrate that the proposed algorithm significantly improves the network communication quality, as well as has obvious advantages compared to several baselines.