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）的联邦训练因其能在保护数据隐私的同时，于数据隔离场景下执行图相关任务而广受欢迎。然而，联邦GNN系统中的图异质性挑战仍然存在。现有框架通过使用不同统计量表征局部任务，并借助简单的聚合机制关联这些任务来处理该问题。但这些方法在两方面存在效率不足：任务相关性量化的质量较低，以及协作结构利用的低效性。为解决这些问题，我们提出FedGKD，一种新颖的联邦GNN框架，该框架采用创新的客户端图数据集蒸馏方法提取更能描述任务相关性的任务特征，并引入一种新型的、能够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世界数据集上进行了广泛实验，证明了该框架的优越性能。