Towards Personalized Federated Node Classification in One-shot Communication

Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos in distributed private graphs data management. In practical scenarios involving complex and heterogeneous distributed graph data, personalized Federated Graph Learning (pFGL) aims to enhance model utility by training personalized models tailored to individual client needs, rather than relying on a universal global model. However, existing pFGL methods often require numerous communication rounds under heterogeneous client graphs, leading to significant security concerns and communication overhead. While One-shot Federated Learning (OFL) addresses these issues by enabling collaboration in a single round, existing OFL methods are designed for image-based tasks and ineffective for graph data, leaving a critical gap in the field. Additionally, personalized models often suffer from bias, failing to generalize effectively to minority data. To address these challenges, we propose the first one-shot personalized federated graph learning method for node classification, compatible with the Secure Aggregation protocol for privacy preservation. Specifically, for effective graph learning in a single communication round, our method estimates and aggregates class-wise feature distribution statistics to construct a global pseudo-graph on the server, facilitating the training of a global graph model. Moreover, to mitigate bias, we introduce a two-stage personalized training approach that adaptively balances local personal information and global insights from the pseudo-graph, improving both personalization and generalization. Extensive experiments conducted on 8 multi-scale graph datasets demonstrate that our method significantly outperforms state-of-the-art baselines across various settings.