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. Specifically, we formulate SAE optimization for edge anomaly detection as a multi-objective optimization problem and propose MO-SAE (Multi-Objective Stacked AutoEncoders). The multiple objectives are addressed by integrating model clipping, multi-branch exit design, and a matrix approximation technique. In addition, a multi-objective heuristic algorithm is employed to effectively balance the competing objectives in SAE optimization. Our results demonstrate that the proposed MO-SAE delivers substantial improvements over the original approach. On the x86 architecture, it reduces storage space and power consumption by at least 50%, improves runtime efficiency by no less than 28%, and achieves an 11.8% compression rate, all while maintaining application performance. Furthermore, MO-SAE runs efficiently on edge devices with ARM architecture. Experimental results show a 15% improvement in inference speed, facilitating efficient deployment in cloud-edge collaborative anomaly detection systems.

翻译：堆叠自编码器（SAE）在边缘异常检测场景中已被广泛采用。然而，SAE的资源密集型特性对边缘设备构成了重大挑战，这些设备通常资源受限且必须快速适应动态变化的环境。优化SAE以满足实际部署场景的异构需求，包括受限存储下的高性能、低功耗、快速推理和高效模型更新，仍然是一个重大挑战。为解决这一问题，我们提出了一个集成优化框架，联合考虑这些关键因素以实现平衡且自适应的系统级优化。具体而言，我们将面向边缘异常检测的SAE优化建模为一个多目标优化问题，并提出了MO-SAE（多目标堆叠自编码器）。该框架通过集成模型剪枝、多分支退出设计和矩阵近似技术来处理多个优化目标。此外，采用了一种多目标启发式算法以有效平衡SAE优化中的竞争目标。实验结果表明，所提出的MO-SAE相比原始方法取得了显著改进。在x86架构上，其存储空间和功耗降低至少50%，运行效率提升不低于28%，并实现了11.8%的压缩率，同时保持了应用性能。此外，MO-SAE在ARM架构的边缘设备上也能高效运行。实验显示推理速度提升15%，有助于在云边协同异常检测系统中实现高效部署。