Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
It addresses privacy issues for educational applications, but the approach is incremental as it applies an existing technique to a new domain.
This paper tackles the problem of privacy concerns in data-driven education by evaluating federated learning for educational data prediction, finding it achieves comparable predictive accuracy and greater resilience under adversarial attacks compared to non-federated approaches.
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.