# Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning

arXiv: https://arxiv.org/abs/2503.13550 · HTML: https://mlsanity.com/paper/2503.13550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning](https://mlsanity.com/paper/2108.10241/) (2021)
- [Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics](https://mlsanity.com/paper/2401.06883/) (2024)
- [Defending against the Label-flipping Attack in Federated Learning](https://mlsanity.com/paper/2207.01982/) (2022)
- [Advancing privacy in learning analytics using differential privacy](https://mlsanity.com/paper/2501.01786/) (2025)
- [Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms](https://mlsanity.com/paper/2501.01785/) (2025)
- [Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation](https://mlsanity.com/paper/2501.01793/) (2025)

## Built on by (most cited first, 1 of 1)

- [Learning to Attack: Uncovering Privacy Risks in Sequential Data Releases](https://mlsanity.com/paper/2510.24807/) (2025)

## Abstract (arXiv)

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.