Federated learning (FL) is a promising approach to enable the future Internet of vehicles
consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and …

Abstract In Heterogeneous Vehicle-to-Everything Networks (HVNs), multiple users such as
vehicles and handheld devices and infrastructure can communicate with each other to …

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 …

This article presents a unified approach to the problem of learning-based optimal control of
connected human-driven and autonomous vehicles in mixed-traffic environments including …

The high performance requirement of task processing and low energy consumption
requirement of network operation result in a contradiction for the Internet of Vehicles (IoV) …

Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety,
efficiency, sustainability, and comfortable services for automated vehicles and road …

Vehicular communications is one of the important applications of 5G communication
systems. The allocation scheme of scarce wireless resources will directly affect the quality of …

We propose an autonomous blockchain-based federated learning (BFL) design for privacy-
aware and efficient vehicular communication networking, where local on-vehicle machine …

Vehicle‐assisted data offloading is envisioned to significantly alleviate the problem of
explosive growth of mobile data traffic. However, due to the high mobility of vehicles and the …

Influenced by the randomnesses in the realistic vehicle-to-infrastructure (V2I) scenarios,
traditional deterministic optimization algorithms cannot be adopted directly to promote …