CSRI-FGL: Cross-Client Semantic Relation Inference for Federated Graph Learning

Federated graph learning (FGL) enables collaborative learning over distributed graphs while keeping raw graph data local. However, fragmented and non-IID local subgraphs make it difficult for existing parameter-aggregation-based methods to capture transferable semantic connection patterns for recovering semantically meaningful missing links. To address this issue, we propose CSRI-FGL, a framework that learns semantic connection patterns for missing-link recovery via LLM-guided relation inference. It distills node-level textual semantics and pairwise textual consistency signals into federatively aggregated edge generators, enabling each client to infer informative links without sharing raw graph data. Specifically, CSRI-FGL first extracts semantic representations and textual consistency signals from local node texts using a client-side LLM module to supervise the construction of local-pattern-enhanced graphs. The server then aggregates client-side edge-generator parameters into a global generator that captures transferable connection patterns across clients, and each client applies this global generator only within its own node set to build a global-pattern-enhanced local graph. Finally, a dual-branch GNN decouples personalized node representation learning from shared semantic connection-pattern learning by separately modeling local-pattern-enhanced and global-pattern-enhanced graphs. Experiments on five node-classification benchmarks under non-IID client partitions demonstrate that CSRI-FGL consistently outperforms representative FGL baselines in overall node-classification performance while maintaining competitive personalized performance.