Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

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 …

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 …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

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 …

Large scale language models (LLM) have received significant attention and found diverse
applications across various domains, but their development encounters challenges in real …

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 …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

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

Federated Learning (FL) deals with learning a central model (ie the server) in privacy-
constrained scenarios, where data are stored on multiple devices (ie the clients). The central …