The role of fast yet reliable wireless communications in various application domains is
getting ever more important. At the same time, as use cases are becoming more and more …

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 …

Given the plethora of sensors with which vehicles are equipped, today's automated vehicles
already generate large amounts of data, and this is expected to increase in the case of …

Federated learning (FL) is a distributed machine learning (ML) framework where several
clients cooperatively train an ML model by exchanging the model parameters without …

Autonomous driving relies greatly on deep learning to comprehend the surroundings and
activities of the road systems. The learning models are traditionally trained off-line and used …

This article presents the concept of federated learning (FL) of eXplainable Artificial
Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems …

The evolution of cars has always been driven by the aim of making transport safer for
passengers and pedestrians, but also the need to improve the livability of the cities …

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 …

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

Federated learning allows multiple users and parties to collaborate and train machine
learning models in a distributed and privacy-preserving manner in Vehicular Adhoc …