Entropy-adaptive differential privacy federated learning for student performance prediction and privacy protection: a case study in Python programming - Chen, Shanwei; Qi, Xiuzhi (Frontiers in Artificial Intelligence, 2025)
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 10 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.