Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the problem by representing local tasks using different statistics and relating them through a simple aggregation mechanism. However, these approaches suffer from limited efficiency from two aspects: low quality of task-relatedness quantification and inefficacy of exploiting the collaboration structure. To address these issues, we propose FedGKD, a novel federated GNN framework that utilizes a novel client-side graph dataset distillation method to extract task features that better describe task-relatedness, and introduces a novel server-side aggregation mechanism that is aware of the global collaboration structure. We conduct extensive experiments on six real-world datasets of different scales, demonstrating our framework's outperformance.

翻译：近年来，图神经网络（GNN）的联邦训练因能在数据隔离场景下执行图相关任务并保护数据隐私而广受欢迎。然而，联邦GNN系统中的图异质性挑战依然存在。现有框架通过使用不同统计量表征局部任务，并通过简单聚合机制关联任务来解决该问题。但这类方法存在两方面效率局限：任务相关性量化质量低，且协作结构利用效果差。为此，我们提出FedGKD——一种新型联邦GNN框架。该框架采用新颖的客户端图数据集蒸馏方法提取能更好描述任务相关性的任务特征，并引入感知全局协作结构的服务器端聚合机制。我们在六个不同规模的真实数据集上进行了大量实验，结果表明该框架具有显著性能优势。