Federated learning (FL), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

Machine learning (ML) algorithms are essential components in autonomous driving. In most
existing connected and autonomous vehicles (CAVs), a large amount of driving data …

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 …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart
decisions, improving vehicular services and enhancing the driving experience through a …

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as
drones) are widely applied in various industries. However, the open nature of 5G networks …

Federated learning (FL) is a promising distributed learning solution that only exchanges
model parameters without revealing raw data. However, the centralized architecture of FL is …

Since the traffic conditions change over time, machine learning models that predict traffic
flows must be updated continuously and efficiently in smart public transportation. Federated …

While the convergence of Artificial Intelligence (AI) techniques with improved information
technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it …

Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial
intelligence while keeping raw training data on local devices. By leveraging blockchain, this …