Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
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.
Cite
@article{arxiv.2503.14799,
title = {Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure},
author = {Fatemeh Dehrouyeh and Ibrahim Shaer and Soodeh Nikan and Firouz Badrkhani Ajaei and Abdallah Shami},
journal= {arXiv preprint arXiv:2503.14799},
year = {2025}
}
Comments
This paper has been accepted for presentation at IEEE ICC 2025. The final published version will be available in the conference proceedings. The implementation and code are available at: https://github.com/Western-OC2-Lab/EVCI-Pruning