In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can
improve the driving experience and service quality. However, the bandwidth, security and …

Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing,
computing and processing. Emerging Big Data and Artificial Intelligence (AI) technologies …

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart
decisions, improving vehicular services and enhancing the driving experience through a …

Federated learning (FL), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

The advances in today's IoT devices and machine learning methods have given rise to the
concept of Federated Learning. Through such a technique, a plethora of network devices …

The accuracy of the ML model is essential for the further development of AI-enabled CAVs.
With the increasing complexity of on-board sensor systems, the large amount of raw data …

Machine learning (ML) algorithms are essential components in autonomous driving. In most
existing connected and autonomous vehicles (CAVs), a large amount of driving data …

The application of artificial intelligence technology in Internet of Vehicles (IoV) has attracted
great research interests with the goal of enabling smart transportation and traffic …

The conventional architecture of vehicular ad hoc networks (VANETs) with a centralized
approach has difficulty overcoming the increasing complexity of intelligent transportation …

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in
a collaborative manner. Previous works focus on data sharing between vehicles, but a large …