Federated Learning in Vehicular Networks

Basit Öğe Kaydı
dc.authorscopusid55362509900
dc.authorscopusid57208713241
dc.authorscopusid9133370600
dc.authorscopusid8720616800
dc.authorscopusid23007914800
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorSoner, B.
dc.contributor.authorÇöleri, S.
dc.contributor.authorGunduz, D.
dc.contributor.authorBennis, M.
dc.date.accessioned2023-07-26T11:53:48Z
dc.date.available2023-07-26T11:53:48Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022 -- 5 September 2022 through 8 September 2022 -- 183951en_US
dc.description.abstractMachine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data trans-mission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks. © 2022 IEEE.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 119E350en_US
dc.description.sponsorshipACKNOWLEDGMENT This work was supported by CHIST-ERA grant CHISTERA-18-SDCDN-001, and the Scientific and Technological Council of Turkey 119E350 and Ford Otosan.en_US
dc.identifier.doi10.1109/MeditCom55741.2022.9928621
dc.identifier.endpage77en_US
dc.identifier.isbn9.78167E+12
dc.identifier.scopus2-s2.0-85142242242en_US
dc.identifier.startpage72en_US
dc.identifier.urihttps://doi.org/10.1109/MeditCom55741.2022.9928621
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12605
dc.indekslendigikaynakScopusen_US
dc.institutionauthorElbir, Ahmet M.
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectedge efficiencyen_US
dc.subjectedge intelligenceen_US
dc.subjectfederated learningen_US
dc.subjectMachine learningen_US
dc.subjectvehicular networksen_US
dc.subjectInformation managementen_US
dc.subjectIntelligent systemsen_US
dc.subjectMachine learningen_US
dc.subjectMotor transportationen_US
dc.subjectObject recognitionen_US
dc.subjectTransmissionsen_US
dc.subjectAutonomous drivingen_US
dc.subjectCentraliseden_US
dc.subjectEdge efficiencyen_US
dc.subjectEdge intelligenceen_US
dc.subjectFederated learningen_US
dc.subjectMachine-learningen_US
dc.subjectObjects detectionen_US
dc.subjectRoad safetyen_US
dc.subjectTransmission overheadsen_US
dc.subjectVehicular networksen_US
dc.subjectObject detectionen_US
dc.titleFederated Learning in Vehicular Networksen_US
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
12605.pdf
Boyut:
342.21 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu