EAGER: Collaborative Research: Understanding Human Behaviors and Mental Health using Federated Machine Learning on Smart Phones

EAGER：协作研究：使用智能手机上的联合机器学习了解人类行为和心理健康

基本信息

批准号：
2041096
负责人：
Hai Phan
金额：
$ 7.5万
依托单位：
New Jersey Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041096&HistoricalAwards=false
关键词：
EAGER Collaborative Research Understanding Human

项目摘要

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 federated learning framework and application. In the first component, the project will collect smartphone-based sensor data from student sub-population in both urban and suburban areas along with other health related surveys and data. The project will specifically analyze and determine what data collected from the mobile phone can be the indictors and causal factors of behavior and mental health. In the second component, the project will develop deep learning models to predict human behaviors, physical and mental health conditions/trends over time, under rigorous privacy protection. Specifically, the prediction models will be developed in federated learning settings to train the model locally on the device with differential privacy guarantees, without collecting sensor data to the cloud. Finally, the project will develop a federated learning based behavior monitoring and prediction application on mobile phones and will evaluate the prototype system on the cohort of studies from first component.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解人类行为和心理健康对现代社会变得越来越重要。冠状病毒（Covid-19）持续爆发不仅进一步强调了其重要性，而且还要求立即采取行动。该项目将在移动设备上开发一个联合的机器学习（FL）框架和应用，以了解人类的行为和心理健康。计划的研究将协同跨学科研究，特别是推动联邦学习和公共卫生的信封。该项目不仅将为联邦学习社区提供一个重要且及时的现实应用程序，即健康行为监测和预测，而且还将通过移动设备以及Covid-19以独特而详细的角度对我们对身心健康的理解以及Covid-19对人类社会的影响。该项目将将跨学科研究结果整合到课程中，并培训来自代表性不足的小组的学生。从技术上讲，该项目有两个主要组成部分：1）数据收集和统计分析，以及2）建立联合学习框架和应用程序。在第一个组件中，该项目将从城市和郊区的学生子人口中收集基于智能手机的传感器数据，以及其他与健康相关的调查和数据。该项目将专门分析并确定从手机收集的数据可能是行为和心理健康的因素。在第二部分中，该项目将开发深度学习模型，以预测人类的行为，身心健康状况/趋势随着时间的推移，在严格的隐私保护下。具体而言，预测模型将在联合学习设置中开发，以在没有将传感器数据收集到云的情况下，在设备上局部训练该模型。最后，该项目将在手机上开发基于联邦学习的行为监控和预测应用，并将评估第一组件的研究的原型系统。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来获得支持的。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness

不公平木马：针对模型公平性的针对性后门攻击

DOI：
10.1109/sds57574.2022.10062890
发表时间：
2022
期刊：
IEEE
影响因子：
0
作者：
Furth, Nicholas;Khreishah, Abdallah;Liu, Guanxiong;Phan, NhatHai;Jararweh, Yaser
通讯作者：
Jararweh, Yaser

Zone-based Federated Learning for Mobile Sensing Data

DOI：
10.1109/percom56429.2023.10099308
发表时间：
2023-03
期刊：
2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)
影响因子：
0
作者：
Xiaopeng Jiang;Thinh On;Nhathai Phan;Hessamaldin Mohammadi;Vijaya Datta Mayyuri;An M. Chen;Ruoming Jin;C. Borcea
通讯作者：
Xiaopeng Jiang;Thinh On;Nhathai Phan;Hessamaldin Mohammadi;Vijaya Datta Mayyuri;An M. Chen;Ruoming Jin;C. Borcea

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

DOI：
10.1109/tmc.2022.3223578
发表时间：
2021-11
期刊：
IEEE Transactions on Mobile Computing
影响因子：
7.9
作者：
Han Hu;Xiaopeng Jiang;Vijaya Datta Mayyuri;An M. Chen;D. Shila;Adriaan Larmuseau;Ruoming Jin;C. Borcea;Nhathai Phan
通讯作者：
Han Hu;Xiaopeng Jiang;Vijaya Datta Mayyuri;An M. Chen;D. Shila;Adriaan Larmuseau;Ruoming Jin;C. Borcea;Nhathai Phan

Continual Learning with Differential Privacy

具有差异隐私的持续学习