Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure

• URL: http://arxiv.org/abs/2503.14799v1
• Date: Wed, 19 Mar 2025 00:18:37 GMT
• Title: Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
• Authors: Fatemeh Dehrouyeh, Ibrahim Shaer, Soodeh Nikan, Firouz Badrkhani Ajaei, Abdallah Shami,
• Abstract summary: This paper investigates a pruning method for anomaly detection in resource-constrained environments, specifically targeting EVCI.<n> optimized models achieved significant reductions in model size and inference times, with only a marginal impact on their performance.<n> Notably, our findings indicate that, in the context of EVCI, pruning and FS can enhance computational efficiency while retaining critical anomaly detection capabilities.
• Score: 8.29566258132752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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