This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

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 paper, we propose a multi-agent federated reinforcement learning (MAFRL)-based resource allocation framework for a multi-UAV-enabled healthcare system. We formulate the computation offloading and resource allocation problems as a Markov decision process game in federated learning with multiple participants. Then, we propose a MAFRL algorithm to solve the formulated problem, minimize latency and energy consumption, and ensure the quality of service. Finally, extensive simulation results on a real-world heartbeat dataset prove that the proposed MAFRL algorithm significantly minimizes the cost, preserves privacy, and improves accuracy compared to the baseline learning algorithms.

To appear in IEEE In recent years, Federated Edge Learning has gained interest from both industry and academia for deployment at the wireless network edge. However, some resource-restricted edge devices (EDs) bear more computation and communication loads due to the heterogeneity of data and resources. Several approaches have been proposed in the literature to reduce energy costs by scheduling only a few EDs to complete training tasks based on their energy budgets. Nevertheless, from a practical perspective, the incongruent data distribution cannot be captured, resulting in a biased model for EDs that are frequently selected. Furthermore, the frequently scheduled devices deplete their energy quickly, making them inaccessible. Thus, this paper proposes a novel scheduling policy based on the historical participation of each ED that ensures an unbiased model while balancing learning tasks so that all EDs consume equivalent energy at the end of the training. We formulate an optimization problem based on Jain’s fairness index, followed by tractable algorithms to solve this problem. Extensive experiments have been conducted, and the results show that the proposed algorithm balances the energy consumption among EDs and accelerates the convergence rate while achieving satisfactory performance.

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients’ data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associated with AFBS  based on the quality of the data and its demand in order to maximize learning efficiency and accuracy. This work aims to minimize the computation costs of the IoMT devices while considering UAV resources and the fairness of UAV coverage. Simulation results prove that our proposed algorithm outperforms other baseline algorithms in learning accuracy and computational cost.