Federated learning enables multiple clients to learn a general model without sharing local
data, and the federated learning system also improves information security and advances …

Recently, federated graph learning has attracted significant attention, as subgraphs of a
global graph may often distribute across different institutions and are subject to privacy …

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 …

Federated Learning (FL) empowers multiple clients to collaboratively learn a global
generalization model without the need to share their local data, thus reducing privacy risks …

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 …

Abstract Federated Graph Learning (FGL) opens up new possibilities for machine learning
in complex networks and distributed training, enabling multiple clients to collaborate on …

Since the concept of federated learning (FL) was proposed by Google in 2017, many
applications have been combined with FL technology due to its outstanding performance in …

Abstract Graph Neural Networks (GNN) have developed rapidly and solved a wide range of
graph-related tasks. One advantage of GNN over traditional neural networks is the utilization …

GNNs have achieved remarkable performance on graph classification tasks. It can be
attributed to the accessibility of abundant graph data, which are usually isolated by different …

Mining graph data has gained wide attention in modern applications. With the explosive
growth of graph data, it is common to see many of them collected and stored in different …