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In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of Medical Things (IoMT) have been used in the healthcare industry to monitor remote users’ health status, such as elderly monitoring, injuries, stress, and patients with chronic diseases. Since IoMT devices have limited resources, mobile edge computing (MEC) has been deployed in 5G networks to enable them to offload their tasks to the nearest computational servers for processing. However, when IoMTs are far from network coverage or the computational servers at the terrestrial MEC are overloaded/emergencies occur, these devices cannot access computing services, potentially risking the lives of patients. In this context, unmanned aerial vehicles (UAVs) are considered a prominent aerial connectivity solution for healthcare systems. In this article, we propose a multiagent federated reinforcement learning (MAFRL)-based …