FedN-TAGC: Federated Node-Level Text-Attributed Graph Clustering

Node-level federated graph clustering studies how several holders of unlabeled subgraphs can train clustering models without sharing local graph data. Existing methods usually rely on graph topology or vector attributes, but many real graphs also attach texts to nodes. In text-attributed graphs, a node's text and its local neighborhood may point to different clusters, which can corrupt local cluster evidence and cross-client semantic alignment. We propose FedN-TAGC, a Federated Node-Level Text-Attributed Graph Clustering method for this setting. On each client, FedN-TAGC learns text-view, structure-view, and fused node representations, and compares text-view and structure-view assignments under the same fused cluster centers to separate stable samples from structure-text conflict cases. Stable samples are summarized by an LLM into local cluster descriptions, while conflict cases are assigned after the server forms global topics. The server groups uploaded summaries into global topics, aligns client clusters, and sends semantic consensus back to guide later local clustering. Experiments on benchmark text-attributed graphs show that this conflict-handling design improves federated node clustering.