解决联邦图异常检测中数据异构导致的泛化差问题
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
- 基于正常图重构，避免使用合成异常数据
- 客户端节点贡献门控+服务器滑动窗口聚类，提升个性化能力
- 适合隐私敏感场景下的跨机构图异常检测
图级异常检测（GLAD）对于保障图驱动应用的可靠性至关重要，能识别偏离多数模式的异常图。在分布式场景下，隐私顾虑促使联邦图级异常检测（FedGLAD）成为无需共享原始数据即可协同检测的可行方案。然而，现有方法因依赖不现实的合成异常且在数据异构下个性化能力不足，导致泛化性能差。为此，本文提出一种新的联邦图级异常检测方法FedCIGAR，采用基于重构的范式，在正常图上训练以避免合成数据；同时引入客户端节点贡献门控机制和服务器端滑动窗口聚类策略，有效应对数据异构。大量实验表明，相比最先进方法，FedCIGAR在性能与鲁棒性上均表现更优。
原文摘要 · Abstract (English)
Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate from the majority. Considering the privacy concerns in distributed scenarios, federated graph-level anomaly detection (FedGLAD) has emerged as a promising solution to enable collaborative detection without sharing raw data. However, existing methods suffer from poor generalization due to the reliance on unrealistic synthetic anomalies and insufficient personalization capabilities under data heterogeneity. To address these challenges, we propose a novel Federated graph-level anomaly detection approach with Cluster-adaptIve GAted Reconstruction (FedCIGAR). Specifically, we design a reconstruction-based paradigm trained on normal graphs to avoid synthetic data. Furthermore, we introduce a client-side node contribution gating mechanism and a server-side sliding window-based clustering strategy to tackle data heterogeneity. Extensive experiments demonstrate that FedCIGAR achieves superior performance and robustness in contrast to state-of-the-art methods.
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