ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1653437
Entropy-Adaptive Differential Privacy Federated Learning for Student Performance Prediction and Privacy Protection: A Case Study in Python Programming
Provisionally accepted
Shan Wei Chen*
Xiuzhi Qi- Baoji University of Arts and Sciences, Baoji, China
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In the context of the digital transformation of engineering education, protecting student data privacy has become a key challenge for enabling data-driven instruction. This study proposes an Entropy-Adaptive Differential Privacy Federated Learning method (EADP-FedAvg) to enhance the accuracy of student performance prediction while ensuring data privacy. Based on online test records from Python programming courses for Electronic Engineering students (grade 2021-2023) at the School of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, China, the study uses a Multilayer Perceptron (MLP) model and ten distributed clients for training. Under different privacy budgets (ε = 0.1, 1e-6, and 1.0), EADP-FedAvg achieves a test accuracy of 92.7%, macro-average score of 92.1%, and entropy of 0.207, outperforming standard federated learning and approaching centralized learning performance. The results demonstrate that by adaptively adjusting the noise level based on output entropy, EADP-FedAvg effectively balances privacy preservation and model accuracy. This method offers a novel solution for analyzing privacy-sensitive educational data in engineering education.
Keywords: Federated learning, Entropy-Adaptive Differential Privacy, Student performance prediction, Distributed data analysis, Python programming
Received: 25 Jun 2025; Accepted: 19 Aug 2025.
Copyright: © 2025 Chen and Qi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shan Wei Chen, Baoji University of Arts and Sciences, Baoji, China
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