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Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Feb. 19, 2024, 5:42 a.m. | Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv case case study cs.cr cs.lg data data-driven demographics detection differential health health conditions impacts importance life mental health monitoring privacy quality stress study transfer transfer learning type
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