MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection
Stacked AutoEncoders (SAE) have been widely adopted in edge anomaly detection scenarios. However, the resource-intensive nature of SAE can pose significant challenges for edge devices, which are typically resource-constrained and must adapt rapidly to dynamic and changing conditions. Optimizing SAE to meet the heterogeneous demands of real-world deployment scenarios, including high performance under constrained storage, low power consumption, fast inference, and efficient model updates, remains a substantial challenge. To address this, we propose an integrated optimization framework that jointly considers these critical factors to achieve balanced and adaptive system-level optimization. S...
| Authors | Lizhao Zhang, Shengsong Kong, Tao Guo et al. |
|---|---|
| Primary category | cs.NE |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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