Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for mental health monitoring, existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications. In this paper, we introduce a differential private federated transfer learning framework for mental health monitoring to enhance data privacy and enrich data sufficiency. To accomplish this, we integrate federated learning with two pivotal elements: (1) differential privacy, achieved by introducing noise into the updates, and (2) transfer learning, employing a pre-trained universal model to adeptly address issues of data imbalance and insufficiency. We evaluate the framework by a case study on stress detection, employing a dataset of physiological and contextual data from a longitudinal study. Our finding show that the proposed approach can attain a 10% boost in accuracy and a 21% enhancement in recall, while ensuring privacy protection.

翻译：心理健康问题普遍存在于不同人群中，亟需有效监测以减轻其对生活质量的负面影响。数据驱动型心理健康监测方法的兴起，凸显了隐私保护技术在处理敏感健康数据中的重要性。尽管联邦学习在心理健康监测领域已取得进展，但现有方法仍难以应对特定网络攻击的脆弱性及实际应用中的数据不足问题。本文提出一种基于差分隐私的联邦迁移学习框架用于心理健康监测，旨在增强数据隐私性并提升数据充分性。为实现这一目标，我们将联邦学习与两个关键要素相结合：（1）差分隐私，通过向更新值中添加噪声实现；（2）迁移学习，利用预训练的通用模型有效解决数据不平衡与不足问题。我们基于纵向研究的生理与情境数据，以压力检测为例对该框架进行案例评估。研究结果表明，所提方法在保障隐私保护的同时，可实现准确率提升10%、召回率提升21%的效能优化。