AI RESEARCH
Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
arXiv CS.LG
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ArXi:2603.07507v1 Announce Type: new 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