Privacy-preserving multimodal student well-being analytics via federated temporal transformers

Abstract Student well-being analytics may support timely educational assistance, but behavioural, psychological, and academic records are sensitive and difficult to pool. We present a federated temporal Transformer that combines smartphone sensing, ecological momentary assessment (EMA), academic records, and digital behavioural traces while retaining raw records on student clients. Because psychometric variables can overlap with a composite well-being target, evaluation is organized around a leakage-free multimodal protocol, a temporally separated forecasting protocol, and a full-feature diagnostic upper bound. On 48 StudentLife participants, student-level leave-one-student-out predictions yielded an area under the receiver operating characteristic curve (AUROC) of 0.834 for centralized training and 0.824 for FedProx ( $$p=0.21$$ ). Record-level differentially private training at $$\\varepsilon =4$$ achieved an AUROC of 0.791; the corresponding membership-inference AUROC decreased from 0.92 without privacy noise to 0.64. Including target-overlapping survey variables inflated AUROC by 0.094–0.107, demonstrating why the leakage-free protocol is used for the main claims. Directional findings were reproduced on 497 independent GLOBEM participants, where FedProx achieved an AUROC of 0.783. Cross-modal ablation, calibration, privacy attacks, external validation, and explanation-stability analyses jointly characterize predictive utility and deployment limits. The framework is intended for research and educational risk stratification, not clinical diagnosis or autonomous intervention.