Summary of Differential Private Federated Transfer Learning For Mental Health Monitoring in Everyday Settings: a Case Study on Stress Detection, by Ziyu Wang et al.
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
by Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed differential private federated transfer learning framework for mental health monitoring combines two key techniques: differential privacy to ensure data privacy and transfer learning to address data imbalance and insufficiency. This approach is evaluated through a case study on stress detection using physiological and contextual data from a longitudinal study, showing a 10% boost in accuracy and 21% enhancement in recall while maintaining privacy protection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to monitor mental health conditions by combining two important ideas: keeping personal information private and using existing knowledge to make better predictions. This helps keep people’s data safe and ensures that the program is good at detecting stress, which can improve life quality. |
Keywords
* Artificial intelligence * Recall * Transfer learning
