Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancing
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 32 references that are in the map. First posted on arXiv 23 Aug 2025.
Builds on
5 of 32 references.
References · 5
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2022Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learningcited by 13
- 2025A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProxcited by 4
- 2024Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfercited by 2
- 2025Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Featurescited by 1
Built on by
Not cited yet.