用联邦学习保护学生隐私，实现高效个性化推荐
Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction
- 采用联邦学习框架，不集中数据即可训练推荐模型
- 在ASSISTments数据集上达成76.28%的F1分数，达中心化模型92%性能
- 适合关注教育数据隐私的平台与研究者使用
教育数字化带来个性化推荐机遇，但也引发学生数据隐私挑战。传统推荐系统依赖集中式数据，与现代数据保护法规冲突。本文提出一种基于联邦学习（FL）的隐私保护推荐系统，利用大规模ASSISTments数据集中的深度特征和深度神经网络（DNN）进行建模。通过严格对比多种联邦聚合策略，发现FedProx相比标准的FedAvg在异构学生数据下更稳定有效。优化后的联邦模型达到76.28%的F1得分，相当于强大集中式XGBoost模型92%的性能。结果表明，联邦学习可在不集中敏感数据的前提下实现高效内容推荐，为现代教育平台提供兼顾个性化与隐私的可行解决方案。
原文摘要 · Abstract (English)
The increasing digitalization of education presents unprecedented opportunities for data-driven personalization, but it also introduces significant challenges to student data privacy. Conventional recommender systems rely on centralized data, a paradigm often incompatible with modern data protection regulations. A novel privacy-preserving recommender system is proposed and evaluated to address this critical issue using Federated Learning (FL). The approach utilizes a Deep Neural Network (DNN) with rich, engineered features from the large-scale ASSISTments educational dataset. A rigorous comparative analysis of federated aggregation strategies was conducted, identifying FedProx as a significantly more stable and effective method for handling heterogeneous student data than the standard FedAvg baseline. The optimized federated model achieves a high-performance F1-Score of 76.28%, corresponding to 92% of the performance of a powerful, centralized XGBoost model. These findings validate that a federated approach can provide highly effective content recommendations without centralizing sensitive student data. Consequently, our work presents a viable and robust solution to the personalization-privacy dilemma in modern educational platforms.
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