Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

Kang Tan; Duncan Bremner; Julien Le Kernec; Muhammad Imran

doi:10.1109/UCET51115.2020.9205482

Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

Kang Tan
, Duncan Bremner
, Julien Le Kernec
, Muhammad Imran

University of Glasgow

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

43 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2020 International Conference on UK-China Emerging Technologies, UCET 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728194882
DOIs	https://doi.org/10.1109/UCET51115.2020.9205482
State	Published - Aug 2020
Externally published	Yes
Event	2020 International Conference on UK-China Emerging Technologies, UCET 2020 - Glasgow, United Kingdom Duration: 20 Aug 2020 → 21 Aug 2020

Publication series

Name	2020 International Conference on UK-China Emerging Technologies, UCET 2020

Conference

Conference	2020 International Conference on UK-China Emerging Technologies, UCET 2020
Country/Territory	United Kingdom
City	Glasgow
Period	20/08/20 → 21/08/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

5G
Federated Learning
Machine Learning
Mobile Edge Computing
Vehicular Networks

Access to Document

10.1109/UCET51115.2020.9205482

Cite this

Tan, K., Bremner, D., Kernec, J. L., & Imran, M. (2020). Federated Machine Learning in Vehicular Networks: A summary of Recent Applications. In 2020 International Conference on UK-China Emerging Technologies, UCET 2020 Article 9205482 (2020 International Conference on UK-China Emerging Technologies, UCET 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UCET51115.2020.9205482

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title = "Federated Machine Learning in Vehicular Networks: A summary of Recent Applications",

abstract = "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.",

keywords = "5G, Federated Learning, Machine Learning, Mobile Edge Computing, Vehicular Networks",

author = "Kang Tan and Duncan Bremner and Kernec, \{Julien Le\} and Muhammad Imran",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on UK-China Emerging Technologies, UCET 2020 ; Conference date: 20-08-2020 Through 21-08-2020",

year = "2020",

month = aug,

doi = "10.1109/UCET51115.2020.9205482",

language = "English",

series = "2020 International Conference on UK-China Emerging Technologies, UCET 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 International Conference on UK-China Emerging Technologies, UCET 2020",

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}

Tan, K, Bremner, D, Kernec, JL & Imran, M 2020, Federated Machine Learning in Vehicular Networks: A summary of Recent Applications. in 2020 International Conference on UK-China Emerging Technologies, UCET 2020., 9205482, 2020 International Conference on UK-China Emerging Technologies, UCET 2020, Institute of Electrical and Electronics Engineers Inc., 2020 International Conference on UK-China Emerging Technologies, UCET 2020, Glasgow, United Kingdom, 20/08/20. https://doi.org/10.1109/UCET51115.2020.9205482

Federated Machine Learning in Vehicular Networks: A summary of Recent Applications. / Tan, Kang; Bremner, Duncan; Kernec, Julien Le et al.
2020 International Conference on UK-China Emerging Technologies, UCET 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9205482 (2020 International Conference on UK-China Emerging Technologies, UCET 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2020/8

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N2 - 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.

AB - 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.

KW - 5G

KW - Federated Learning

KW - Machine Learning

KW - Mobile Edge Computing

KW - Vehicular Networks

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M3 - Conference contribution

AN - SCOPUS:85094326132

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BT - 2020 International Conference on UK-China Emerging Technologies, UCET 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Tan K, Bremner D, Kernec JL, Imran M. Federated Machine Learning in Vehicular Networks: A summary of Recent Applications. In 2020 International Conference on UK-China Emerging Technologies, UCET 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9205482. (2020 International Conference on UK-China Emerging Technologies, UCET 2020). doi: 10.1109/UCET51115.2020.9205482

Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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