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 …

Federated Learning (FL), which allows multiple participants to co-train machine Learning
models without exposing local data, has been recognized as a promising method in the past …

Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and
convenient. However, in the present, there are some problems in IoV, such as data silos and …

The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out
new possibilities in boosting the service quality for the emerging applications through data …

While the convergence of Artificial Intelligence (AI) techniques with improved information
technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it …

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

Federated Learning (FL) relies on on-device training to avoid the migration of devices' data
to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios …

In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of
concern. The privacy problem is even more severe in autonomous driving business than the …

Internet of Vehicles (IoV) has been sought as a solution to realize an Intelligent
Transportation System (ITS) for efficient traffic management. Data driven ITS requires …

Federated learning enables data owners to jointly train a neural network without sharing
their personal data, which makes it possible to share sensitive data generated from various …