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：心理健康状况在不同人群中普遍存在，迫切需要有效的监测手段来减轻其对生活质量的负面影响。随着数据驱动方法在心理健康监测中的兴起，处理敏感健康数据时的隐私保护技术日益凸显其重要性。尽管联邦学习在心理健康监测领域已取得进展，现有方法仍面临对特定网络攻击的脆弱性以及实际应用中数据不足等挑战。本文提出了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，以增强数据隐私性并提升数据充分性。为此，我们将联邦学习与两个关键要素相结合：（1）差分隐私，通过向更新中引入噪声实现；（2）迁移学习，利用预训练的通用模型有效解决数据不平衡和不足的问题。我们通过一项针对压力检测的案例研究，使用来自纵向研究的生理与情境数据集对该框架进行了评估。研究表明，所提方法在确保隐私保护的前提下，准确率提升了10%，召回率提升了21%。