FedRGL: Federated Riemannian Graph Learning in Mixed-Curvature Spaces with Ricci-Gated Convolution

Federated Graph Learning (FGL) has emerged as an efficient paradigm to address the pronounced data heterogeneity common in real-world decentralized graph datasets, attracting significant interest from both academia and industry. Existing FGL methods often degrade the global model's performance due to a fundamental mismatch between the model's fixed-geometry embedding space and the diverse geometric structures of client data. To address this issue, we propose a novel framework for Personalized Federated Riemannian Graph Learning, namely FedRGL. FedRGL introduces a personalized mixed-curvature product space for each heterogeneous client, mapping local graph data into a tailored geometric space composed of Euclidean, hyperbolic, and spherical manifolds. Furthermore, it leverages a Ricci-Gated Graph Convolutional Network to dynamically adapt its message-passing mechanism to the local topology of each graph. Extensive experiments across diverse geometric heterogeneity settings demonstrate that FedRGL significantly outperforms state-of-the-art FGL methods in terms of model accuracy and generalization.