Federated learning assisted multi-UAV networks
Federated learning assisted multi-UAV networks
Unmanned aerial vehicles (UAVs) have been recognized as a promising technology to be used in a wide range of civilian, public and military applications. However, given their limited payload and flight time, multiple UAVs may have to be harnessed for accomplishing complex high-level tasks, where a control center can be employed for coordinating their actions. In this article, we consider image classification tasks in UAV-aided exploration scenarios, where the coordination of multiple UAVs is implemented by a ground fusion center (GFC) positioned in a strategic, but inaccessible location, such as a mountain top, where recharging the battery is uneconomical or may even be infeasible. On-board cameras are carried by each UAV and then, federated learning (FL) is invoked for reducing the communication cost between the UAVs and the GFC, and the computational complexity imposed on the GFC. In our proposed FL-aided classification approach, initially local training is performed by each UAV based on the locally collected images to create a local model. Then, each UAV sends its locally acquired model to the GFC via a fading wireless channel, where a global model is generated, which is then fed back to each UAV for the next round of their local training. In order to further minimize the computational complexity imposed on the GFC by the UAVs, weighted zero-forcing (WZF) transmit precoding (TPC) is used at each UAV based on realistic imperfect channel state information (CSI). The system performance attained is evaluated by simulations, showing that the proposed system is capable of attaining a high classification accuracy at relatively low communication cost.
Federated learning, convolutional neural network, deep learning, imperfect CSI, multi-class classification, unmanned aerial vehicle
14104-14109
Zhang, Hongming
ebd930db-9cd8-43ff-8b73-92c1d7f0108b
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
November 2020
Zhang, Hongming
ebd930db-9cd8-43ff-8b73-92c1d7f0108b
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhang, Hongming and Hanzo, Lajos
(2020)
Federated learning assisted multi-UAV networks.
IEEE Transactions on Vehicular Technology, 69 (11), , [9210077].
(doi:10.1109/TVT.2020.3028011).
Abstract
Unmanned aerial vehicles (UAVs) have been recognized as a promising technology to be used in a wide range of civilian, public and military applications. However, given their limited payload and flight time, multiple UAVs may have to be harnessed for accomplishing complex high-level tasks, where a control center can be employed for coordinating their actions. In this article, we consider image classification tasks in UAV-aided exploration scenarios, where the coordination of multiple UAVs is implemented by a ground fusion center (GFC) positioned in a strategic, but inaccessible location, such as a mountain top, where recharging the battery is uneconomical or may even be infeasible. On-board cameras are carried by each UAV and then, federated learning (FL) is invoked for reducing the communication cost between the UAVs and the GFC, and the computational complexity imposed on the GFC. In our proposed FL-aided classification approach, initially local training is performed by each UAV based on the locally collected images to create a local model. Then, each UAV sends its locally acquired model to the GFC via a fading wireless channel, where a global model is generated, which is then fed back to each UAV for the next round of their local training. In order to further minimize the computational complexity imposed on the GFC by the UAVs, weighted zero-forcing (WZF) transmit precoding (TPC) is used at each UAV based on realistic imperfect channel state information (CSI). The system performance attained is evaluated by simulations, showing that the proposed system is capable of attaining a high classification accuracy at relatively low communication cost.
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Accepted/In Press date: 28 September 2020
e-pub ahead of print date: 30 September 2020
Published date: November 2020
Additional Information:
Funding Information:
Manuscript received February 21, 2020; revised July 5, 2020; accepted September 27, 2020. Date of publication September 30, 2020; date of current version November 12, 2020. This work was supported by the Fundamental Research Funds for the Central Universities under Grants 500420837 and 505020134. The work of Lajos Hanzo was supported in part by the Engineering and Physical Sciences Research Council Projects EP/N004558/1, EP/P034284/1, EP/P034284/1, EP/P003990/1 (COALESCE), of the Royal Society’s Global Challenges Research Fund Grant and in part by the European Research Council’s Advanced Fellow Grant QuantCom. The review of this article was coordinated by Dr. Kaigui Bian. (Corresponding author: Lajos Hanzo.) Hongming Zhang is with the School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: zhanghm5685@163.com).
Publisher Copyright:
© 1967-2012 IEEE.
Keywords:
Federated learning, convolutional neural network, deep learning, imperfect CSI, multi-class classification, unmanned aerial vehicle
Identifiers
Local EPrints ID: 444382
URI: http://eprints.soton.ac.uk/id/eprint/444382
ISSN: 0018-9545
PURE UUID: 77937790-cbb6-4e0f-befa-8540dbe6e999
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Date deposited: 16 Oct 2020 16:30
Last modified: 18 Mar 2024 02:36
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Author:
Hongming Zhang
Author:
Lajos Hanzo
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