Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection

arXiv:2402.10862v1 Announce Type: cross
Abstract: 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 …

arxiv case conditions cs.cr cs.lg data data-driven demographics detection federated handling health health conditions life mental mental health monitoring prevalent privacy private quality sensitive settings stress study techniques transfer