Differential Private Federated Transfer Learning for Mental Health
  Monitoring in Everyday Settings: A Case Study on Stress Detection
Ziyu Wang Zhongqi Yang Iman Azimi Amir M. Rahmani
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.