Selective Federated Learning for On-Road Services in Internet-of-Vehicles

Saputra, Y; Nguyen, D; Dinh, H; Dutkiewicz, E

Selective Federated Learning for On-Road Services in Internet-of-Vehicles

Saputra, Y Nguyen, D

Dinh, H

Dutkiewicz, E

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE Global Communications Conference (GLOBECOM), 2022, 00
Issue Date:: 2022-11-10

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Field	Value	Language
dc.contributor.author	Saputra, Y
dc.contributor.author	Nguyen, D https://orcid.org/0000-0003-2659-8648
dc.contributor.author	Dinh, H https://orcid.org/0000-0002-9528-0863
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.date	2021-12-06
dc.date.accessioned	2022-08-09T02:26:07Z
dc.date.available	2022-08-09T02:26:07Z
dc.date.issued	2022-11-10
dc.identifier.citation	2021 IEEE Global Communications Conference (GLOBECOM), 2022, 00
dc.identifier.isbn	978-1-7281-8104-2
dc.identifier.issn	2334-0983
dc.identifier.issn	2576-6813
dc.identifier.uri	http://hdl.handle.net/10453/159798
dc.description.abstract	The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE Global Communications Conference (GLOBECOM)
dc.relation.ispartof	IEEE Global Communications Conference
dc.relation.ispartofseries	IEEE Global Communications Conference
dc.relation.isbasedon	10.1109/GLOBECOM46510.2021.9685190
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Selective Federated Learning for On-Road Services in Internet-of-Vehicles
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Spain
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	recently_added	*
pubs.consider-herdc	true
dc.date.updated	2022-08-09T02:26:06Z
pubs.finish-date	2021-12-11
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-12-06
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.

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http://hdl.handle.net/10453/159798