Future Intelligent Transportation Systems (ITS) can improve on-road safety and
transportation efficiency and vehicular networks (VNs) are essential to enable ITS …

Intelligent transportation systems (ITSs) have been fueled by the rapid development of
communication technologies, sensor technologies, and the Internet of Things (IoT) …

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 …

The vehicular ad hoc networks (VANETs) play a significant role in intelligent transportation
systems (ITS). In recent years, federated learning (FL) has been widely used in VANETs to …

Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …

With the development of the technologies deployed on vehicles, there is a significant
increase in the amount of data, which comes from various applications, such as battery …

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning
paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL …

The emerging advances in personal devices and privacy concerns have given the rise to the
concept of Federated Learning. Federated Learning proves its effectiveness and privacy …

Supported by some of the major revolutionary technologies, such as Internet of Vehicles
(IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks …

Federated learning (FL) is a promising approach to enable the future Internet of vehicles
consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and …