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Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management

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Publicado en:IEEE Journal on Selected Areas in Communications vol. 39, no. 10 (2021), p. 3144
Autor principal: Yang, Helin
Otros Autores: Zhao, Jun, Xiong, Zehui, Kwok-Yan, Lam, Sun, Sumei, Liang, Xiao
Publicado:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Materias:
Accuracy
Reliability aspects
Wireless communications
Servers
Unmanned aerial vehicles
Network reliability
Electronic devices
Privacy
Resource scheduling
Algorithms
Resource management
Machine learning
Data retrieval
Data collection
Distributed processing
Computer networks
Federated learning
Acceso en línea:Citation/Abstract
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Resumen: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
1558-0008
DOI:10.1109/JSAC.2021.3088655
Fuente:Advanced Technologies & Aerospace Database