With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Deep learning (DL)-based intelligent fault diagnosis methods have greatly promoted the
development of the field of fault diagnosis due to their powerful feature extraction ability for …

Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume
and speed, but may not be suitable for high-capacity expressways or during peak traffic …

As a distributed learning approach, federated learning trains a shared learning model over
distributed datasets while preserving the training data privacy. We extend the application of …

Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

In the intelligent transportation system (ITS), speed prediction plays a significant role in
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …

This study proposes a short-term traffic flow prediction model that combines community
detection-based federated learning with a graph convolutional network (GCN) to alleviate …