Federated Graph Learning

Federated graph learning (FGL) addresses the challenge of training graph neural networks (GNNs) on distributed, privacy-sensitive graph data without centralizing it. Current research focuses on mitigating the challenges of data heterogeneity across clients, often employing techniques like adaptive sampling, personalized model training, and graph structure-aware aggregation methods within GNN architectures such as GraphSAGE and HGNNs. This field is significant because it enables collaborative model training on large-scale graphs while preserving data privacy, with applications ranging from social network analysis and recommendation systems to medical diagnosis and cybersecurity.