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FDRL Approach for Association and Resource Allocation in Multi-UAV Air-To-Ground IoMT Network

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posted on 2022-09-01, 21:31 authored by Mohammed AbegazMohammed Abegaz, aiman Erbad, Hayla Nahom, Abdullatif AlbaseerAbdullatif Albaseer, Mohamed Abdallah, Mohsen Guizani

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.


NPRP-Standard (NPRP-S) Thirteen (13th) Cycle grant NPRP13S-0205-200265 from the Qatar National Research Fund


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Submitting Author's Institution

Hamad Bin khalifa University

Submitting Author's Country

  • Qatar