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 this https URL

Bkd-FedGNN 是 FedGNN 后门攻击的基准测试，将图后门攻击分解为触发器生成和注入步骤，并将攻击扩展到节点级联合设置，提供了一个统一的框架，涵盖了节点级和图级分类任务。

Bkd-FedGNN: 联邦图神经网络分类后门攻击基准

Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated  Graph Neural Network

Currently, the federated graph neural network (GNN) has attracted a lot of
attention due to its wide applications in reality without violating the privacy
regulations. Among all the privacy-preserving technologies, the differential
privacy (DP) is the most promising one due to its effectiveness and light
computational overhead. However, the DP-based federated GNN has not been well
investigated, especially in the sub-graph-level setting, such as the scenario
of recommendation system. The biggest challenge is how to guarantee the privacy
and solve the non independent and identically distributed (non-IID) data in
federated GNN simultaneously. In this paper, we propose DP-FedRec, a DP-based
federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to
extend the local graph for each client, and thus solve the non-IID problem.
Most importantly, DP is applied not only on the weights but also on the edges
of the intersection graph from PSI to fully protect the privacy of clients. The
evaluation demonstrates DP-FedRec achieves better performance with the graph
extension and DP only introduces little computations overhead.

提出了 DP-FedRec，一种基于差分隐私的联邦图神经网络，应用了私有集交集（PSI）技术实现子图扩展，保护客户隐私并解决非独立同分布数据问题，在保证客户隐私的同时取得了更好的性能表现。