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 …

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 …

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

A digital twin uses a virtual model of the physical system to fulfill the diverse requirements
(eg, latency, reliability, quality of physical experience) for emerging vehicular network …

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 Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential
to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends …

Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine
learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML …

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 …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

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 …