面向工业自动化的多变量时间序列异常检测中的联邦学习方法 / Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
1️⃣ 一句话总结
本文针对联邦学习中多变量时间序列异常检测的数据挑战，专门设计了一个包含循环动态特性的工业自动化数据集，并在此数据集和公开基准上评估了多种检测方法。
arXiv 提交日期: 2026-05-26
Abstract - Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
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.
源自 arXiv: 2605.27486