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arXiv · 2603.07507

Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts

Matea Marinova·Shashi Raj Pandey·Junya Shiraishi·Martin Voigt Vejling·Valentin Rakovic·Petar Popovski
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Abstract

In this work, we present OCLADS, a novel communication framework with continual learning (CL) for Internet of Things (IoT) anomaly detection (AD) when operating in non-stationary environments. As the statistical properties of the observed data change with time, the on-device inference model becomes obsolete, which necessitates strategic model updating. OCLADS keeps track of data distribution shifts to timely update the on-device IoT AD model. To do so, OCLADS introduces two mechanisms during the interaction between the resource-constrained IoT device and an edge server (ES): i) an intelligent sample selection mechanism at the device for data transmission, and ii) a distribution-shift detection mechanism at the ES for model updating. Experimental results with TinyML demonstrate that our proposed framework achieves high inference accuracy while realizing a significantly smaller number of model updates compared to the baseline schemes.

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Matea Marinova, Shashi Raj Pandey, Junya Shiraishi, Martin Voigt Vejling, Valentin Rakovic, Petar Popovski. 2026-03-08. Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts. https://arxiv.org/abs/2603.07507

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