mlsanityData to 14 Oct 2026

Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding

arXiv 2410.09284 ↗First posted 11 Oct 2024cs.LGcited by 2

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Abstract (arXiv)

In Federated Learning (FL), anomaly detection (AD) is a challenging task due to the decentralized nature of data and the presence of non-IID data distributions. This study introduces a novel federated threshold calculation method that leverages summary statistics from both normal and anomalous data to improve the accuracy and robustness of anomaly detection using autoencoders (AE) in a federated setting. Our approach aggregates local summary statistics across clients to compute a global threshold that optimally separates anomalies from normal data while ensuring privacy preservation. We conducted extensive experiments using publicly available datasets, including Credit Card Fraud Detection, Shuttle, and Covertype, under various data distribution scenarios. The results demonstrate that our method consistently outperforms existing federated and local threshold calculation techniques, particularly in handling non-IID data distributions. This study also explores the impact of different data distribution scenarios and the number of clients on the performance of federated anomaly detection. Our findings highlight the potential of using summary statistics for threshold calculation in improving the scalability and accuracy of federated anomaly detection systems.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

16 of 34 references.

References · 16

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  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
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  9. 2017Federated Multi-Task Learningcited by 672
  10. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  11. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  12. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 147
4 more
  1. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  2. 2022Fast Deep Autoencoder for Federated learningcited by 10
  3. 2023Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networkscited by 7
  4. 2022Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensorscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approachcited by 4
  2. 2026Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devicescited by 0