LLM-enhanced Federated Graph Learning with Geometry-aware Graph Projection and Shared Subspace Aggregation

Federated graph learning (FGL) enables collaborative training without sharing raw graph data, but existing methods remain limited when clients perform different types of graph learning tasks. Recent advances in large language models (LLMs) provide new opportunities, where a promising solution is to project graph data into the LLM input space and leverage their generalization ability for prediction. However, directly applying LLMs to FGL raises two key challenges: (1) enhancing LLM interpretation of graphs with limited client data, and (2) obtaining a generalizable graph projector across heterogeneous client tasks. To address these challenges, we propose a LLM-enhanced federated graph learning framework FedLGS, which consists of two modules, i.e., geometry-aware graph projection (GGP) and shared subspace aggregation (SSA). GGP projects graph representations into complementary Riemannian manifolds with Ollivier-Ricci curvature priors, enabling geometry-aware and semantically enriched inputs for LLMs. SSA applies curvature-weighted whitening and aggregates updates in a shared subspace to achieve geometry-consistent generalization across heterogeneous tasks. Extensive experiments on six datasets demonstrate the effectiveness of FedLGS.