Federated Machine Learning for Monitoring Student Mental Health in Kazakhstan

Gulnaz B. Gulnara B. Ali N.B.
October 2025 Science and Information Organization

International Journal of Advanced Computer Science and Applications
2025 #16 Issue 10 212 - 220 pp.

Federated Learning (FL) offers a privacypreserving and decentralized paradigm for machine learning, making it particularly suitable for analyzing sensitive psychological and physiological data. This study aims to develop and evaluate a federated learning framework for assessing the psycho-emotional well-being of students in Kazakhstani educational institutions, where data privacy and infrastructural constraints pose significant challenges. We benchmark three FL algorithms, such as FedAvg, FedOpt, and FedProx, on heterogeneous, institution-level datasets that combine sleep, dietary, activity, and self-reported emotional measures. Experiments simulate cross-device, non-IID deployments and evaluate convergence, accuracy, and stability across ten communication rounds. Results show that FedProx attains the best trade-off between accuracy and stability under non-IID conditions (peak accuracy is 99.9%), while FedOpt provides faster early convergence, and FedAvg performs well for more homogeneous partitions. The methodological contribution comprises optimized aggregation and adaptive client weighting to mitigate non-IID effects in resource-constrained educational settings. These findings validate FL as a scalable, privacypreserving approach for mental health monitoring in education and support its use for early intervention and resilience tracking. The proposed framework contributes to data-driven mental health policy design in educational systems, addressing both ethical and infrastructural considerations. The study discusses limitations of the simulated setup and outlines directions for broader deployment and cross-silo validation.

data privacy , educational data mining , FedAvg , Federated Learning , FedOpt , FedProx , mental health , non-IID data , psychological analytics

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Department of Computer Engineering, International University of Information Technology, Kazakhstan
College of Computing and Informatics, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia

Department of Computer Engineering
College of Computing and Informatics

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