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 Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

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

Federated learning has emerged as an important paradigm for training machine learning
models in different domains. For graph-level tasks such as graph classification, graphs can …

The fast growth of pre-trained models (PTMs) has brought natural language processing to a
new era, which has become a dominant technique for various natural language processing …

Subgraphs of a larger global graph may be distributed across multiple devices, and only
locally accessible due to privacy restrictions, although there may be links between …

Although remarkable progress has been made by existing federated learning (FL) platforms
to provide infrastructures for development, these platforms may not well tackle the …

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 …

Collaborative filtering (CF) based recommender systems are typically trained based on
personal interaction data (eg, clicks and purchases) that could be naturally represented as …