Federated learning is widely used in the context of wireless networks to protect sensitive
user data. However, centralized federated learning encounters some issues when applied to …

Federated learning, a distributed machine learning framework, can be used in many Internet
of Vehicles (IoV) scenarios to enable privacy-preserving distributed intelligence. While …

To provide various intelligent services in Internet of Vehicles (IoVs), such as autonomous
driving, data sharing technologies enable vehicles to overcome information barriers and …

Privacy-sensitive data is stored in autonomous vehicles, smart devices, or sensor nodes that
can move around with making opportunistic contact with each other. Federation among such …

Abstract Although Federated Deep Learning (FDL) enables distributed machine learning in
the Internet of Vehicles (IoV), it requires multiple clients to upload model parameters, thus …

The Internet of Things (IoT) and edge computing technologies have been rapidly developing
in recent years, leading to the emergence of new challenges in privacy and security …

Along with providing several benefits, the unprecedented growth of connected and
automated vehicles brings worries about damaging cyber attacks. Network-based intrusion …

In the vehicular network, federated learning is an emerging paradigm to train deep learning
models safely. However, the non‐identically independent distributed data collected by …

Sixth-Generation (6G)-based Internet of Everything applications (eg autonomous driving
cars) have witnessed a remarkable interest. Autonomous driving cars using federated …

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional
vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects …