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Title	Federated Learning-Based Misbehavior Detection For The 5G-Enabled Internet Of Vehicles
ID_Doc	26371
Authors	Rani P.; Sharma C.; Ramesh J.V.N.; Verma S.; Sharma R.; Alkhayyat A.; Kumar S.
Year	2024
Published	IEEE Transactions on Consumer Electronics, 70, 2
DOI	http://dx.doi.org/10.1109/TCE.2023.3328020
Abstract	The concept of federated learning (FL) is becoming increasingly popular as a method for training collaborative models without loss the sensitive information. The term has become ubiquitous due to the extensive development of autonomous vehicles. Vehicular Networks and the Internet of Vehicles (IoV) enable cooperative learning through federated learning. It is still necessary to address several technical challenges. In recent years, Federated Learning (FL) has attracted significant interest in various sectors, including smart cities and transportation systems. FL-enabled attack detection for IoVs are still in its infancy. However, to determine the main challenges of deployment in real-world scenarios, there needs to be research efforts from various areas. Performance metrics are used to evaluate the effectiveness of the proposed FL framework. According to experiments, the proposed FL approach detected attacks in IOV networks with a maximum accuracy of 99.72%. In addition to precision, recall, and F1 scores, 99.70%, 99.20%, and 99.26% were achieved. A comparison of the proposed model with the existing model shows that the proposed model is more accurate. © 1975-2011 IEEE.
Author Keywords	5G; Federated learning; intelligent transportation system (ITS); Internet of Vehicles (IoV); vehicular ad-hoc networks (VANETs)

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