Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

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

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge
server leverages highly mobile connected vehicles'(CVs') onboard central processing units …

In this paper, we propose a novel approach for optimal resource management and caching
in ultra-reliable low-latency communication (URLLC)-enabled Internet of Vehicles (IoV) …

We propose a novel task-oriented and semantic-aware heterogeneous networks (TOSA-
HetNets) framework for multitype Artificial Intelligence of Things (AIoT) devices with various …

Federated learning (FL) enables decentralized model training, but the distribution of data
across devices presents significant challenges to global model convergence. Existing …

In the Internet of Vehicles (IoV), data privacy concerns have prompted the adoption of
Federated Learning (FL). Efficiency improvements in FL remain a focal area of research …

The current Internet of Vehicles (IoV) faces significant challenges related to resource
heterogeneity, which adversely impacts the convergence speed and accuracy of federated …

With the advancement of cloud computing technology, the cloud-based personal health
record (CB-PHR) has become an increasingly popular way for modern patients to flexibly …

The emerging integration of Internet of Things (IoT) and AI has unlocked numerous
opportunities for innovation across diverse industries. However, growing privacy concerns …