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 …

Recent years have witnessed a thriving growth of computing facilities connected at the
network edge, cultivating edge networks as a fundamental infrastructure for supporting …

In graph machine learning, data collection, sharing, and analysis often involve multiple
parties, each of which may require varying levels of data security and privacy. To this end …

Graph neural networks (GNN) have been widely deployed in real-world networked
applications and systems due to their capability to handle graph-structured data. However …

Federated graph learning (FGL) has been proposed to collaboratively train the increasing
graph data with graph neural networks (GNNs) in a recommendation system, aggregating …

Federated graph learning (FGL) enables multiple participants with distributed but connected
graph data to collaboratively train a model in a privacy-preserving way. However, the high …

Graph neural networks (GNNs) have been applied successfully in many machine learning
tasks due to their advantages in utilizing neighboring information. Recently, with the global …

We address the challenge of federated learning on graph-structured data distributed across
multiple clients. Specifically, we focus on the prevalent scenario of interconnected …

Federated Graph Learning (FGL) has been developed to enable multiple parties to
collaboratively train a graph neural network while maintaining their local graph data. Despite …

Graph neural networks have shown excellent performance in many fields owing to their
powerful processing ability of graph data. In recent years, federated graph neural network …