Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
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 dete...
| Authors | Matea Marinova, Shashi Raj Pandey, Junya Shiraishi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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