This study proposes and validates a Federated Learning (FL) framework to proactively identify at-risk students while preserving data privacy. Persistently high dropout rates in distance education remain a pressing institutional challenge. Using the large-scale OULAD dataset, we simulate a privacy-centric scenario where models are trained on early academic performance and digital engagement patterns. Our work investigates the practical trade-offs between model complexity (Logistic Regression vs. a Deep Neural Network) and the impact of local data balancing. The resulting federated model achieves strong predictive power (ROC AUC approximately 85%), demonstrating that FL is a practical and scalable solution for early-warning systems that inherently respects student data sovereignty.

翻译：本研究提出并验证了一种联邦学习（FL）框架，用于在保护数据隐私的前提下主动识别风险学生。远程教育中持续较高的辍学率仍是一个紧迫的机构性挑战。利用大规模OULAD数据集，我们模拟了一个以隐私为中心的场景，其中模型基于早期学业表现和数字参与模式进行训练。本研究探讨了模型复杂度（逻辑回归与深度神经网络）与本地数据平衡影响之间的实际权衡。最终构建的联邦模型展现出强大的预测能力（ROC AUC约85%），表明联邦学习是一种实用且可扩展的解决方案，适用于本质上尊重学生数据主权的早期预警系统。