Federated Learning with Limited Node Labels

arXiv:2406.12435v1 Announce Type: new
Abstract: Subgraph federated learning (SFL) is a research methodology that has gained significant attention for its potential to handle distributed graph-structured data. In SFL, the local model comprises graph neural networks (GNNs) with a partial graph structure. However, some SFL models have overlooked the significance of missing cross-subgraph edges, which can lead to local GNNs being unable to message-pass global representations to other parties' GNNs. Moreover, existing SFL models require substantial labeled data, which limits their …

abstract arxiv attention cs.ai cs.dc cs.lg data distributed federated learning gnns graph graph neural networks however labels methodology networks neural networks node potential research significance structured data type