联邦学习在保护隐私前提下，仅损失2.7%准确率即可预测学生成绩。
Centralized vs. Federated Learning for Educational Data Mining: A Comparative Study on Student Performance Prediction with SAEB Microdata
- 用联邦学习模拟50所学校协作训练模型，避免数据集中存储。
- 中心化模型准确率63.96%，联邦模型最高达61.23%。
- 适合注重数据隐私的教育机构或受严格法规约束的地区使用。
人工智能在教育中的应用为个性化学习和早期识别高风险学生提供了巨大潜力，但隐私法规（如巴西通用数据保护法LGPD）限制了敏感学生数据的集中管理。为此，本文评估了联邦学习（尤其是FedProx算法）在巴西基础教育评估系统（SAEB）微数据上预测学生成绩的可行性与有效性。采用深度神经网络（DNN）以联邦方式训练，模拟50所学校参与，与中心化梯度提升树（XGBoost）模型进行对比。基于超过两百万名学生的数据集分析显示，中心化模型准确率为63.96%，而联邦模型最高达到61.23%，性能仅下降2.73%。结果表明，联邦学习可在满足巴西数据保护法规的前提下，有效构建协作式预测模型。
原文摘要 · Abstract (English)
The application of data mining and artificial intelligence in education offers unprecedented potential for personalizing learning and early identification of at-risk students. However, the practical use of these techniques faces a significant barrier in privacy legislation, such as Brazil's General Data Protection Law (LGPD), which restricts the centralization of sensitive student data. To resolve this challenge, privacy-preserving computational approaches are required. The present study evaluates the feasibility and effectiveness of Federated Learning, specifically the FedProx algorithm, to predict student performance using microdata from the Brazilian Basic Education Assessment System (SAEB). A Deep Neural Network (DNN) model was trained in a federated manner, simulating a scenario with 50 schools, and its performance was rigorously benchmarked against a centralized eXtreme Gradient Boosting (XGBoost) model. The analysis, conducted on a universe of over two million student records, revealed that the centralized model achieved an accuracy of 63.96%. Remarkably, the federated model reached a peak accuracy of 61.23%, demonstrating a marginal performance loss in exchange for a robust privacy guarantee. The results indicate that Federated Learning is a viable and effective solution for building collaborative predictive models in the Brazilian educational context, in alignment with the requirements of the LGPD.
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