Privacy-preserving federated learning for interpretable student at-risk prediction across schools.

Early-warning systems for at-risk students increasingly rely on predictive models trained on sensitive educational records. However, centralized learning pipelines raise concerns about privacy, institutional data sovereignty, and auditability, particularly when student-level data are shared across institutions. This study presents Federated Learning for At-Risk Student Prediction with Differential Privacy and Proof-Before-Train (FL-AtRisk-DP-PBT), a federated learning framework for multi-school at-risk prediction that integrates Federated Averaging (FedAvg)-based training, client-side DP, and a PBT protocol for verifiable logging of client participation and model states. The framework uses a single interpretable global logistic-regression classifier and is evaluated under centralized, standard federated, and FL + DP+PBT regimes on three educational datasets: a primary merged cohort of 14,003 students partitioned into 10 simulated schools, the xAPI-Edu-Data click-stream corpus, and the Students Performance in Exams dataset. On the primary dataset, the centralized model achieves 99.14% accuracy, F1 = 0.9915, and area under the curve (AUC) = 0.9998, while the FedAvg and FL + DP+PBT variants achieve 98.61%/0.9863/0.9993 and 98.00%/0.9802/0.9992, respectively. On xAPI and Exams, FL + DP+PBT reaches approximately 93-94% accuracy, F1 ≈ 0.92-0.93, and AUC ≈ 0.97-0.98. Coefficient-based feature-importance analysis indicates that FL + DP+PBT preserves broadly similar interpretation patterns to the centralized and non-private federated baselines. The PBT ablation introduces only small metric changes relative to DP-only federated training. Overall, the results suggest that interpretable federated at-risk prediction can retain competitive utility while keeping student records local and adding privacy-preserving and verifiable training mechanisms. These findings should be interpreted within the evaluated datasets, simulated school partitions, and label definitions.