针对充电桩异常检测，用剪枝和特征选择压缩模型，提速省资源。
Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
- 用剪枝与SHAP特征选择优化三种模型
- 模型体积和推理时间大幅下降，性能损失小
- 适合资源受限的物联网设备部署
随着物联网设备对实时处理的需求增长，优化机器学习模型的大小、延迟和计算效率至关重要。本文研究了一种适用于资源受限环境的剪枝方法，专门用于电动汽车充电基础设施（EVCI）中的异常检测。基于CICEVSE2024数据集，我们通过Optuna进行超参数调优，训练并优化了三种模型：多层感知机（MLP）、长短期记忆网络（LSTM）和XGBoost，随后采用基于SHAP的特征选择（FS）和非结构化剪枝技术进一步精炼模型。优化后的模型在保持性能的前提下显著减小了模型尺寸并降低了推理时间。结果表明，在EVCI场景下，剪枝与特征选择可在不牺牲关键异常检测能力的前提下提升计算效率。
原文摘要 · Abstract (English)
With the growing need for real-time processing on IoT devices, optimizing machine learning (ML) models' size, latency, and computational efficiency is essential. This paper investigates a pruning method for anomaly detection in resource-constrained environments, specifically targeting Electric Vehicle Charging Infrastructure (EVCI). Using the CICEVSE2024 dataset, we trained and optimized three models-Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost-through hyperparameter tuning with Optuna, further refining them using SHapley Additive exPlanations (SHAP)-based feature selection (FS) and unstructured pruning techniques. The optimized models achieved significant reductions in model size and inference times, with only a marginal impact on their performance. Notably, our findings indicate that, in the context of EVCI, pruning and FS can enhance computational efficiency while retaining critical anomaly detection capabilities.
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