Understanding human behaviors and mental health are becoming increasingly important for modern society. The ongoing outbreak of coronavirus (COVID-19) not only further highlights its importance but also calls for immediate action. This project will develop a federated machine-learning (FL) framework and application on mobile device for understanding human behaviors and mental health. The planned research will synergize interdisciplinary research and particularly push the envelopes of federated learning and public health. This project will not only provide an important and timely real-world application, health-behavior monitoring and prediction, for the federated learning community, but also will advance our understanding of physical and mental health through mobile devices, and the impacts of COVID-19 to human society in a unique and detailed angle. This project will integrate the interdisciplinary research results into courses, and train students from underrepresented groups. 

Technically, the project has two main components: 1) Data collection and statistical analysis, and 2) Building a federated learning framework and application. 

In the first component, the project collected smartphone-based sensor data from student sub-populations in urban and suburban areas and other health-related surveys and data. This study aimed to assess the correlation of depression and anxiety with time spent at home among students at two universities, one urban and one suburban, during the COVID-19 pandemic. Our study found time spent at home had a positive correlation with the mental health of urban students, but a negative correlation with suburban students, and stronger correlations between female depression, anxiety, and time at home were significant. This project also reveals the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression, r/Anxiety, r/SuicideWatch) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods. 

In the second component, the project developed an open eco-system of federated learning framework and algorithms to optimize computational resources, including quantization, gradient fusion across geographical zones, and fairness guarantees, with models to predict human behaviors, physical and mental health conditions/trends over time, under rigorous privacy protection. Specifically, the prediction models were developed in federated learning settings to train the model locally on mobile devices with differential privacy and fairness guarantees without collecting sensor data to the cloud. The project discovered new vulnerabilities associated with federated learning to strengthen the robustness of federated learning models against backdoor and fairness attacks. Finally, the project developed a federated learning-based behavior monitoring and prediction application on mobile phones and evaluated the prototype system on the cohort of studies from the first component.

Last Modified: 07/18/2024
Modified by: Ruoming Jin