Abstract Internet of Vehicles (IoVs) consists of smart vehicles, Autonomous Vehicles (AVs)
as well as roadside units (RSUs) that communicate wirelessly to provide enhanced …

Data sharing and content offloading among vehicles is an imperative part of the Internet of
Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly …

Video surveillance in intelligent transportation systems (ITSs) is in the rapid growth stage,
where video analytics is a potential technology to improve the safety of the Internet of …

Autonomous Vehicles (s) take advantage of Machine Learning (ML) for yielding improved
experiences of self-driving. However, large-scale collection of s' data for training will …

In recent years, the powerful combination of Multi-access Edge Computing (MEC) and
Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent …

Vehicular ad hoc networks (VANETs) have become an essential part in smart transportation
systems of modern cities. However, because of dynamicity and infrastructure-less of …

Model migration can accelerate model convergence during federated learning on the
Internet of Things (IoT) devices and reduce training costs by transferring feature extractors …

The Internet of Vehicles connects all vehicles and shares dynamic vehicular data via
wireless communications to effectively control vehicles and improve traffic efficiency …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

A rapid-growing machine learning technique called federated edge learning has emerged to
allow a massive number of edge devices (eg smart phones) to collaboratively train globally …