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Abstract
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.
Original language | English |
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Pages (from-to) | 1417-1422 |
Number of pages | 6 |
Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Rio de Janeiro, Brazil Duration: 4 Dec 2022 → 8 Dec 2022 |
Keywords
- IoMT
- UAV
- computation cost
- federated learning
- privacy preservation
- resource allocation
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Dive into the research topics of 'FDRL Approach for Association and Resource Allocation in Multi-UAV Air-To-Ground IoMT Network'. Together they form a unique fingerprint.Projects
- 1 Finished
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EX-QNRF-NPRPS-38: AI-Based Next Generation Edge Platform for Heterogeneous Services using 5G Technologies
Abdallah, M. M. (Principal Investigator), Abegaz, M. S. (Post Doctoral Fellow), Hevesli, M. (Graduate Student), Student-1, G. (Graduate Student), Saad, M. R. (Consultant), Assistant-1, R. (Research Assistant), Assistant-3, R. (Research Assistant), Mohamed, D. A. (Principal Investigator), Al-Jaber, D. H. (Principal Investigator), Chiasserini, P. C. F. (Principal Investigator) & Al Fuqaha, A. (Lead Principal Investigator)
11/04/21 → 30/09/24
Project: Applied Research