HETEROGENEOUS FEDERATED GRAPH LEARNING VIA OPTIMIZER SECOND-MOMENT COORDINATION

Federated graph learning (FGL) has emerged as a promising approach to train graph neural networks on distributed graph data. However, existing methods often cause personalized learning degradation and convergence failure due to mismatch fixed second moments of gradients on different clients with heterogeneous graph dataset. To address this issue, we propose a novel federated graph learning framework, namely Fedseed, which leverage the second moment of Adam optimizer as statistical metric of multi-hop graphs feature. On the client side, Fedseed designs an adaptive gradient update mechanism to adapt local heterogeneity, which dynamically balance the local and global second moment on each client per round. On the server side, Fedseed proposes a similarity aggregation strategy to mitigate model drift by heterogeneity, which aggregates different local second moments by clustering. Extensive experiments demonstrate that Fedseed significantly outperforms state-of-the-art methods on accuracy over real-world graph datasets with statistical and topological heterogeneity settings.