Lianglin Hu

Rethinking Federated Graph Learning: A Data Condensation Perspective

Federated graph learning is a widely recognized technique that promotes collaborative training of graph neural networks (GNNs) by multi-client graphs.However, existing approaches heavily rely on the communication of model parameters or gradients for federated optimization and fail to adequately addr