MOTION: Multi-Sculpt Evolutionary Coarsening for Federated Continual Graph Learning

Guancheng Wan (University of California, Los Angeles) · Wenke Huang (Wuhan University) · Guibin Zhang (International Digital Economy Academy, International Digital Economy Academy) · Bo Du (Wuhan University) · Mang Ye (Wuhan University) · Fengyuan Ran (Wuhan University) · Ruikang Zhang (Tongji University) · Xuankun Rong (Wuhan University) · Yuxin Wu (Renmin University of China)

average accuracyaverage forgetting ratedecentralized clientsdynamic graphsfederated continual graph learninggeneralization graph neural networks graph topologygraph topology-preserving multi-sculpt coarseninggraph-aware evolving parameter adaptive engineparameter conflictsrobustnessserver-side aggregationsimilarity-guided fusiontopology-sensitive compatibility matrix

Graph neural networks (GNNs) have achieved remarkable success in various domains but typically rely on centralized, static graphs, which limits their applicability in distributed, evolving environments. To address this limitation, we define the task of Federated Continual Graph Learning (FCGL), a paradigm for incremental learning on dynamic graphs distributed across decentralized clients. Existing methods, however, neither preserve graph topology during task transitions nor mitigate parameter conflicts in server‐side aggregation. To overcome these challenges, we introduce **MOTION**, a generalizable FCGL framework that integrates two complementary modules: the Graph Topology‐preserving Multi‐Sculpt Coarsening (G‐TMSC) module, which maintains the structural integrity of past graphs through a multi‐expert, similarity‐guided fusion process, and the Graph‐Aware Evolving Parameter Adaptive Engine (G‐EPAE) module, which refines global model updates by leveraging a topology‐sensitive compatibility matrix. Extensive experiments on real‐world datasets show that our approach improves average accuracy (AA) by an average of 30\% $\uparrow$ over the FedAvg baseline across five datasets while maintaining a negative $\downarrow$ average forgetting (AF) rate, significantly enhancing generalization and robustness under FCGL settings. The code is available for anonymous access at https://anonymous.4open.science/r/MOTION.