Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …

The development of Intelligent Transportation System (ITS) has brought about
comprehensive urban traffic information that not only provides convenience to urban …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Achieving fully autonomous driving with heightened safety and efficiency depends on
vehicle-to-everything (V2X) cooperative perception (CP), which allows vehicles to share …

A self-driving vehicle can navigate autonomously in smart cities without the need for human
intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a …

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine
Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments …

Multimodal federated learning (FL) aims to enrich model training in FL settings where clients
are collecting measurements across multiple modalities. However, key challenges to …

Federated Learning (FL), as a distributed machine learning paradigm, facilitates
collaborative training without sharing raw data and holds promise for effective application in …

Federated Learning (FL), as a typical distributed machine learning framework, has been
effectively applied to traffic flow optimization, driving behavior analysis, and other areas …

This paper explores new sensor technologies and their integration within Connected
Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like …