Abstract Internet of Vehicles (IoV) generates large amounts of data at the network edge.
Machine learning models are often built on these data, to enable the detection …

Federated learning (FL) is widely used in various fields because it can guarantee the privacy
of the original data source. However, in data-sensitive fields such as Internet of Vehicles …

Federated Learning (FL) enables collaborative and privacy-preserving training of machine
learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles …

With the increasing number of connected vehicles on the road, vehicular communications
have become an important research area. Federated learning (FL), a distributed machine …

Driven by technologies such as mobile edge computing and 5G, recent years have
witnessed the rapid development of urban informatics, where a large amount of data is …

Federated learning (FL) is a promising technology for vehicular networks to protect vehicles'
privacy in Internet of Vehicles (IoV). Vehicles with limited computation capacity may face a …

As many countries have promulgated laws to protect users' data privacy, how to legally use
users' data has become a hot topic. With the emergence of federated learning (also known …

In Internet-of-Vehicles (IoVs), Federated Learning (FL) is increasingly used by smart
vehicles to process various sensing data. FL is a collaborative learning approach that …

Federated learning is an enabling technology for the services in Internet of vehicles because
it can effectively alleviate privacy issues in data circulation and diversified intelligent …

Federated learning is an emerging distributed learning paradigm which brings an efficient
and privacy-preserving intelligent model for the Internet of Vehicles (IoV). Unfortunately …