In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Federated learning (FL) plays an important role in the development of smart cities. With the
evolution of big data and artificial intelligence, issues related to data privacy and protection …

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …

Objective. Federated learning (FL) is a computational paradigm that enables organizations
to collaborate on machine learning (ML) and deep learning (DL) projects without sharing …

Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in
learning distributed representations from graph-structured data. However, centralizing a …

Federated learning (FL) is a computational paradigm that enables organizations to
collaborate on machine learning (ML) projects without sharing sensitive data, such as …

Federated Learning (FL) is an approach to conduct machine learning without centralizing
training data in a single place, for reasons of privacy, confidentiality or data volume …

The incredible development of federated learning (FL) has benefited various tasks in the
domains of computer vision and natural language processing, and the existing frameworks …

Federated learning has attracted much research attention due to its privacy protection in
distributed machine learning. However, existing work of federated learning mainly focuses …

Vast amount of data generated from networks of sensors, wearables, and the Internet of
Things (IoT) devices underscores the need for advanced modeling techniques that leverage …