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.
@article{arxiv.2603.07507,
title = {Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts},
author = {Matea Marinova and Shashi Raj Pandey and Junya Shiraishi and Martin Voigt Vejling and Valentin Rakovic and Petar Popovski},
journal= {arXiv preprint arXiv:2603.07507},
year = {2026}
}
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Manuscript submitted to EUSIPCO 2026. The copyright might be transferred without further notice