Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly growing research topic, as it integrates the strengths of graph neural networks and federated learning to enable advanced machine learning applications without direct access to sensitive data. Despite its advantages, the distributed nature of FedGNN introduces additional vulnerabilities, particularly backdoor attacks stemming from malicious participants. Although graph backdoor attacks have been explored, the compounded complexity introduced by the combination of GNNs and federated learning has hindered a comprehensive understanding of these attacks, as existing research lacks extensive benchmark coverage and in-depth analysis of critical factors. To address these limitations, we propose Bkd-FedGNN, a benchmark for backdoor attacks on FedGNN. Specifically, Bkd-FedGNN decomposes the graph backdoor attack into trigger generation and injection steps, and extending the attack to the node-level federated setting, resulting in a unified framework that covers both node-level and graph-level classification tasks. Moreover, we thoroughly investigate the impact of multiple critical factors in backdoor attacks on FedGNN. These factors are categorized into global-level and local-level factors, including data distribution, the number of malicious attackers, attack time, overlapping rate, trigger size, trigger type, trigger position, and poisoning rate. Finally, we conduct comprehensive evaluations on 13 benchmark datasets and 13 critical factors, comprising 1,725 experimental configurations for node-level and graph-level tasks from six domains. These experiments encompass over 8,000 individual tests, allowing us to provide a thorough evaluation and insightful observations that advance our understanding of backdoor attacks on FedGNN.The Bkd-FedGNN benchmark is publicly available at https://github.com/usail-hkust/BkdFedGCN.

翻译：联邦图神经网络（FedGNN）是近年快速发展的研究热点，它融合了图神经网络与联邦学习的优势，能够在无需直接访问敏感数据的情况下实现先进的机器学习应用。尽管具有诸多优势，但FedGNN的分布式特性引入了额外脆弱性，尤其是来自恶意参与方的后门攻击。尽管已有图后门攻击的相关研究，但图神经网络与联邦学习相结合带来的复合复杂性，导致现有研究缺乏广泛的基准测试覆盖和对关键因素的深入分析，制约了对这些攻击的全面理解。为解决上述局限，我们提出Bkd-FedGNN——一种面向FedGNN后门攻击的基准测试框架。具体而言，Bkd-FedGNN将图后门攻击解耦为触发器生成与注入两个步骤，并将攻击扩展至节点级联邦场景，形成覆盖节点级与图级分类任务的统一框架。此外，我们系统研究了影响FedGNN后门攻击的多个关键因素，这些因素被划分为全局级与局部级两类，包括数据分布、恶意攻击者数量、攻击时机、重叠率、触发器尺寸、触发器类型、触发器位置及投毒率。最后，我们在13个基准数据集和13个关键因素上开展全面评估，涵盖6个领域的节点级与图级任务的1725种实验配置。这些实验包含超过8000次独立测试，从而为深入理解FedGNN后门攻击提供了详实的评估与富有洞见的观察结论。Bkd-FedGNN基准测试代码已公开于https://github.com/usail-hkust/BkdFedGCN。