mlsanityData to 7 Oct 2026

Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation

arXiv 2605.27486 ↗First posted 26 May 2026cs.LGcited by 2

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Abstract (arXiv)

Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection methods within FL paradigm poses data-centric challenges. The existing datasets do not counteract these challenges since they do not simultaneously provide sufficient scale, accurate labels, and freedom from common flaws. In addition, the role of cyclic process behavior, which is common in discrete industrial automation, remains underexplored for MTSAD for the current state of research. This paper aims to shed more light on the literature and address these gaps by introducing a dataset designed with cyclic dynamics arising from the repetitive nature of discrete automation processes and evaluates selected MTSAD methods on both the proposed dataset and a public benchmark dataset.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 26 May 2026.

Builds on

13 of 31 references.

References · 13

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  4. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  5. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  6. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
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  9. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  10. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  11. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  12. 2022Deep Federated Anomaly Detection for Multivariate Time Series Datacited by 3
1 more
  1. 2024A Generative Model Based Honeypot for Industrial OPC UA Communicationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEscited by 1
  2. 2026Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detectioncited by 0