Federated Machine Learning in Vehicular Networks: a Summary of Recent Applications

Tan, K. , Bremner, D. , Le Kernec, J. and Imran, M. (2020) Federated Machine Learning in Vehicular Networks: a Summary of Recent Applications. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi: 10.1109/UCET51115.2020.9205482)

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Future Intelligent Transportation Systems (ITS) can improve on-road safety and transportation efficiency and vehicular networks (VNs) are essential to enable ITS applications via information sharing. The development of 5G introduces new technologies providing improved support for connected vehicles through highly dynamic heterogeneous networks. Machine Learning (ML) can capture the high dynamics of VNs but the distributed data cause new challenges for ML hence requires distributed solutions. Federated learning (FL), a distributed ML framework, gives a distributed ML framework while ensuring information privacy protection and is an exciting area to explore in VNs. This article provides a detailed summary of recent FL applications in VNs and gives insights on current research challenges. The included research topics are resource management, performance optimization and applications based on VNs.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Le Kernec, Dr Julien and Tan, Kang and Bremner, Dr Duncan
Authors: Tan, K., Bremner, D., Le Kernec, J., and Imran, M.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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