Telematics technology development offers vehicles a range of intelligent and convenient
functions, including navigation and mapping services, intelligent driving assistance, and …

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 …

As a promising distributed learning paradigm, Federated Learning (FL) is expected to meet
the ever-increasing needs of Machine Learning (ML) based applications in Intelligent …

The sixth-generation network (6G) is expected to achieve a fully connected world, which
makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging …

To exploit the massive amounts of onboard data in vehicular networks while protecting data
privacy and security, federated learning (FL) is regarded as a promising technology to …

Vehicular networks enable vehicles support some real-time applications through training
data. Due to the limited computing capability of vehicles, vehicles usually transmit data to a …

As a promising distributed technology, federated learning (FL) has been widely used in
vehicular networks involving large amounts of IoT-enabled sensor data, which derives …

Recently, federated learning (FL) has received intensive research because of its ability in
preserving data privacy for scattered clients to collaboratively train machine learning …

In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide
customized applications for Connected and Autonomous Vehicles (CAVs). The …

Federated Learning (FL) has been recognized as a transformative approach in vehicular
networks, enabling collaborative training between vehicles and preserving data privacy …