On a Federated-Learning-Based Computation Offloading Strategy for Nonterrestrial-Network-Assisted Internet of Medical Things

With the rapid growth of Internet of Medical Things (IoMT) devices and the advancement of medical large language models, smart healthcare applications, including regular vital sign monitoring, medical intervention, and medication adherence tracking, play an increasingly important role in the near future. As the data generated by smart healthcare applications usually have strict requirements for latency and privacy, traditional data processing in a centralized manner is not the preferred choice, especially for users in remote areas. In this article, we consider the nonterrestrial networks composed of Low Earth Orbit satellites and autonomous aerial vehicles (AAVs) to realize seamless coverage. A federated reinforcement learning-based hierarchical computation offloading strategy is proposed to make distinct decisions and protect data privacy. Moreover, we optimize the communication efficiency between IoMT devices, AAVs, and satellites, improve task processing capabilities, and reduce network load by reasonably setting task priorities. The simulation results show that the proposed method performs well in reducing energy consumption and shortening task completion time, significantly improving resource utilization and achieving system load balancing.