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Title: Privacy-preserving federated learning for UAV-enabled networks: learning-based joint scheduling and resource management
Authors: Yang, Helin
Zhao, Jun
Xiong, Zehui
Lam, Kwok-Yan
Sun, Sumei
Xiao, Liang
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Yang, H., Zhao, J., Xiong, Z., Lam, K., Sun, S. & Xiao, L. (2021). Privacy-preserving federated learning for UAV-enabled networks: learning-based joint scheduling and resource management. IEEE Journal On Selected Areas in Communications, 39(10), 3144-3159.
Project: RG128/18 
Journal: IEEE Journal on Selected Areas in Communications 
Abstract: Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.
ISSN: 0733-8716
DOI: 10.1109/JSAC.2021.3088655
Schools: School of Computer Science and Engineering 
Research Centres: Nanyang Technopreneurship Center 
Strategic Centre for Research in Privacy-Preserving Technologies & Systems (SCRIPTS)
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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