Federated Learning for Pedestrian Detection in Vehicular Networks
Kumec, Feyzi Ege; Reyhanoglu, Aslihan; Kar, Emrah; Turan, Bugra; Coleri, Sinem; Bennis, Mehdi; Elgabli, Anis; Gunduz, Deniz; Karaagac, Sercan (2023-11-06)
IEEE
06.11.2023
F. E. Kumec et al., "Federated Learning for Pedestrian Detection in Vehicular Networks," 2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, Turkiye, 2023, pp. 150-154, doi: 10.1109/BlackSeaCom58138.2023.10299783
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061481
https://urn.fi/URN:NBN:fi:oulu-202502061481
Tiivistelmä
Abstract
Vehicular connectivity is foreseen to increase road safety by enabling connected vehicle applications. On the other hand, machine learning (ML) methods are provisioned to increase road safety by supporting object detection and assisted driving. Recently, distributed ML methods, which rely on data transmission between a parameter server and vehicular edge devices, are introduced to develop intelligent transportation systems. In this paper, we investigate the feasibility of the usage of a distributed ML algorithm, federated learning (FL), to detect pedestrians by using vehicular networks. We first provide a comprehensive overview of the proposed scheme, then highlight the methodology to enable FL-based pedestrian detection from the images obtained by vehicle cameras. We further present experimental validation results for communication resource utilization, and pedestrian detection accuracy by using convolutional neural networks (CNNs) and deep neural networks (DNNs) layers in our model architecture for an FL scheme. We obtain 90% pedestrian detection accuracy with our FL scheme.
Vehicular connectivity is foreseen to increase road safety by enabling connected vehicle applications. On the other hand, machine learning (ML) methods are provisioned to increase road safety by supporting object detection and assisted driving. Recently, distributed ML methods, which rely on data transmission between a parameter server and vehicular edge devices, are introduced to develop intelligent transportation systems. In this paper, we investigate the feasibility of the usage of a distributed ML algorithm, federated learning (FL), to detect pedestrians by using vehicular networks. We first provide a comprehensive overview of the proposed scheme, then highlight the methodology to enable FL-based pedestrian detection from the images obtained by vehicle cameras. We further present experimental validation results for communication resource utilization, and pedestrian detection accuracy by using convolutional neural networks (CNNs) and deep neural networks (DNNs) layers in our model architecture for an FL scheme. We obtain 90% pedestrian detection accuracy with our FL scheme.
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