The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions

2025-01-20

AI Summary

This study addresses privacy risks inherent in AI modeling of student mental health within educational settings, identifying unique challenges posed by educational data silos, cross-institutional heterogeneity, and stringent regulatory compliance requirements. Method: We propose a phased, human-centered federated learning (FL) adoption roadmap tailored for educational institutions, integrating multi-source privacy regulations (e.g., FERPA, GDPR) and co-design principles to enable short- and long-term synergistic evolution of FL-based psychological support systems. Contribution: We introduce the first FL-driven taxonomy for educational mental health applications, pinpoint critical technical bottlenecks—including model convergence under non-IID student data and adaptive personalization—and delineate ethical implementation pathways. Our framework establishes a scalable, privacy-by-design paradigm for distributed mental health modeling, offering both theoretical foundations and actionable guidelines for developing secure, trustworthy intelligent psychological support systems in campus environments.

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence -- the critical years of high school and college -- investigating AI/ML-driven mental health solutions within the education domain is of paramount importance. Nevertheless, conventional AI/ML techniques follow a centralized model training architecture, which poses privacy risks due to the need for transferring students' sensitive data from institutions, universities, and clinics to central servers. Federated learning (FL) has emerged as a solution to address these risks by enabling distributed model training while maintaining data privacy. Despite its potential, research on applying FL to analyze students' mental health remains limited. In this paper, we aim to address this limitation by proposing a roadmap for integrating FL into mental health data analysis within educational settings. We begin by providing an overview of mental health issues among students and reviewing existing studies where ML has been applied to address these challenges. Next, we examine broader applications of FL in the mental health domain to emphasize the lack of focus on educational contexts. Finally, we propose promising research directions focused on using FL to address mental health issues in the education sector, which entails discussing the synergies between the proposed directions with broader human-centered domains. By categorizing the proposed research directions into short- and long-term strategies and highlighting the unique challenges at each stage, we aim to encourage the development of privacy-conscious AI/ML-driven mental health solutions.

Problem

• Privacy Protection
• Artificial Intelligence
• Mental Health in Students

Innovation

• Federated Learning
• Student Mental Health
• Privacy-Preserving AI/ML