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Federated learning assisted multi-UAV networks

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 paper, 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.
0018-9545
Zhang, Hongming
ebd930db-9cd8-43ff-8b73-92c1d7f0108b
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
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. (doi:10.1109/TVT.2020.3028011).

Record type: Article

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 paper, 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

Identifiers

Local EPrints ID: 444382
URI: http://eprints.soton.ac.uk/id/eprint/444382
ISSN: 0018-9545
PURE UUID: 77937790-cbb6-4e0f-befa-8540dbe6e999
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 16 Oct 2020 16:30
Last modified: 16 Oct 2020 16:30

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Contributors

Author: Hongming Zhang
Author: Lajos Hanzo ORCID iD

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