Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and complexity of graph-structured data in many real-world scenarios. However, existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases, thereby impeding collaboration among organizations seeking to collectively develop robust GAD models. Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants. To tackle these challenges, we propose an effective federated graph anomaly detection framework (FGAD). We first introduce an anomaly generator to perturb the normal graphs to be anomalous, and train a powerful anomaly detector by distinguishing generated anomalous graphs from normal ones. Then, we leverage a student model to distill knowledge from the trained anomaly detector (teacher model), which aims to maintain the personality of local models and alleviate the adverse impact of non-IID problems. Moreover, we design an effective collaborative learning mechanism that facilitates the personalization preservation of local models and significantly reduces communication costs among clients. Empirical results of the GAD tasks on non-IID graphs compared with state-of-the-art baselines demonstrate the superiority and efficiency of the proposed FGAD method.

翻译：图异常检测旨在识别显著偏离其他图的异常图，由于图结构数据在众多现实场景中的广泛存在及其复杂性，该方法日益受到关注。然而，现有图异常检测方法通常采用集中式训练，在敏感场景下可能导致隐私泄露风险，从而阻碍组织间协作开发鲁棒的图异常检测模型。尽管联邦学习提供了有前景的解决方案，但普遍存在的非独立同分布问题和高通信成本带来了显著挑战，尤其在参与者间分布图数据的协作场景中更为突出。为应对这些挑战，我们提出一种高效的联邦图异常检测框架。首先引入异常生成器将正常图扰动为异常图，通过区分生成的异常图与正常图来训练强大的异常检测器；其次，利用学生模型从训练好的异常检测器（教师模型）中蒸馏知识，旨在保持局部模型的个性化并缓解非独立同分布问题的不利影响；此外，我们设计了一种高效的协作学习机制，既能促进局部模型的个性化保留，又可显著降低客户端的通信成本。在非独立同分布图上的图异常检测任务中，与最先进基线方法的对比实验结果表明，所提出的FGAD方法具有优越性和高效性。