Federated Learning in Vehicular Networks
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Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Machine 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.
Açıklama
2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022 -- 5 September 2022 through 8 September 2022 -- 183951
Anahtar Kelimeler
edge efficiency, edge intelligence, federated learning, Machine learning, vehicular networks, Information management, Intelligent systems, Machine learning, Motor transportation, Object recognition, Transmissions, Autonomous driving, Centralised, Edge efficiency, Edge intelligence, Federated learning, Machine-learning, Objects detection, Road safety, Transmission overheads, Vehicular networks, Object detection
Kaynak
2022 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022
