Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated
  Graph Neural Network
Fan Liu Siqi Lai Yansong Ning Hao Liu
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.