mlsanityData to 7 Oct 2026

FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

arXiv 2605.09428 ↗First posted 10 May 2026cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 10 May 2026.

Builds on

27 of 44 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  5. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  6. 2021Federated Graph Classification over Non-IID Graphscited by 113
  7. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  8. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  9. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  10. 2020On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insightscited by 53
  11. 2022Raising the Bar in Graph-level Anomaly Detectioncited by 45
  12. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
15 more
  1. 2025BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attackscited by 37
  2. 2024Federated Graph Semantic and Structural Learningcited by 30
  3. 2023Graph-level Anomaly Detection via Hierarchical Memory Networkscited by 16
  4. 2025Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approachcited by 14
  5. 2022From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approachcited by 13
  6. 2024Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedycited by 13
  7. 2025Bisecle: Binding and Separation in Continual Learning for Video Language Understandingcited by 13
  8. 2025A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Modelscited by 13
  9. 2026DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learningcited by 13
  10. 2025Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Timecited by 12
  11. 2026OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Modelscited by 12
  12. 2025FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detectioncited by 10
  13. 2025Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detectioncited by 8
  14. 2026From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detectioncited by 8
  15. 2026Towards One-for-All Anomaly Detection for Tabular Datacited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Towards Anomaly Detection on Relational Datacited by 4
  2. 2026CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detectioncited by 3
  3. 2026DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Modelscited by 0
  4. 2026SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detectioncited by 0
  5. 2026HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolationcited by 0
  6. 2026DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detectioncited by 0