标题： 从集中式机器学习到教育中心理健康联邦学习的转变：当前方法与未来方向的综述 显示英文标题

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

摘要： 研究越来越多地探索人工智能（AI）和机器学习（ML）在心理健康领域的应用，以提高患者护理和医疗保健提供者的效率。 鉴于心理健康问题经常出现在青少年早期——高中和大学的关键时期——在教育领域研究由AI/ML驱动的心理健康解决方案至关重要。 然而，传统的AI/ML技术遵循集中式模型训练架构，由于需要将学生的敏感数据从机构、大学和诊所传输到中央服务器，这会带来隐私风险。 联邦学习（FL）作为一种解决方案出现，通过在保持数据隐私的同时实现分布式模型训练来解决这些风险。 尽管其潜力巨大，但将FL应用于分析学生心理健康的研究仍然有限。 在本文中，我们旨在通过提出一个路线图来解决这一局限性，将FL整合到教育环境中的心理健康数据分析中。 我们首先概述学生中的心理健康问题，并回顾现有研究，其中ML已被用于解决这些问题。 接下来，我们探讨FL在心理健康领域的更广泛应用，以强调对教育背景关注的缺乏。 最后，我们提出了专注于使用FL解决教育部门心理健康问题的有前景的研究方向，这包括讨论所提议方向与更广泛的人本主义领域之间的协同效应。 通过将提出的研究所方向分为短期和长期策略，并突出每个阶段的独特挑战，我们旨在鼓励开发注重隐私的AI/ML驱动的心理健康解决方案。 显示英文摘要

摘要： 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.