用联邦学习保护教育数据隐私，预测效果不降反强。
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
- 在不集中数据的前提下，通过联邦学习实现教育预测
- 预测准确率与传统方法相当，且抗攻击能力更强
- 适合关注数据隐私的教育科技研发与管理者
教育领域中数据驱动应用（如学习分析、AI教育）的普及引发了严重的隐私与数据保护问题。尽管此前研究已广泛讨论这些挑战，但实际可行的解决方案仍有限。联邦学习作为一种新兴的隐私保护技术备受关注，但在教育领域的应用仍较少。本文实验评估了联邦学习在教育数据预测中的表现，对比了传统非联邦方法。结果表明，联邦学习在预测准确率上与传统方法相当；在对抗攻击下，其鲁棒性显著优于非联邦设置。研究证实，联邦学习是平衡教育场景中预测性能与隐私保护潜力的重要途径。
原文摘要 · Abstract (English)
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. While these challenges have been widely discussed in previous works, there are still limited practical solutions. Federated learning has recently been discoursed as a promising privacy-preserving technique, yet its application in education remains scarce. This paper presents an experimental evaluation of federated learning for educational data prediction, comparing its performance to traditional non-federated approaches. Our findings indicate that federated learning achieves comparable predictive accuracy. Furthermore, under adversarial attacks, federated learning demonstrates greater resilience compared to non-federated settings. We summarise that our results reinforce the value of federated learning as a potential approach for balancing predictive performance and privacy in educational contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。