Federated Graph Learning with Local Message Compensation

Federated Graph Learning (FGL) aims to collaboratively train Graph Neural Networks (GNNs) across distributed subgraphs with a primary challenge of missing cross-client connections. Existing approaches predominantly rely on a retrieval-based paradigm, which reconstructs missing context by accessing external information across clients. However, such cross-client transmissions inevitably expand the attack surface for privacy inference and incur significant communication overhead. In this work, we propose \textbf{FedLMC}, a novel framework that eliminates the necessity of any node information sharing and prior global adjacency knowledge. FedLMC exploits a dual-stage message compensation mechanism to generate informative and expressive node representations. For the semantic information loss of external neighbors, we propose Local Semantic Compensation, which exploits semantic expansion via semantic surrogates to compensate for missing semantic context. For the structural information loss induced by semantic expansion, we propose Community Structural Compensation, which exploits structural anchoring via community anchors to compensate for underlying structural context. Extensive experiments on twelve datasets (both homophilic and heterophilic) demonstrate that FedLMC establishes a new state-of-the-art performance with superior generalization and robustness to highly fragmented graph data.