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

• 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

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.

对现代社会来说，理解人类行为和心理健康正变得越来越重要。冠状病毒(新冠肺炎)的持续暴发不仅进一步突显了其重要性，而且要求立即采取行动。该项目将开发一个联合机器学习(FL)框架和移动设备上的应用程序，以理解人类行为和心理健康。计划中的研究将协同跨学科研究，特别是推动联合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而及时的真实世界应用--健康行为监测与预测，而且将从独特而详细的角度增进我们对移动设备身心健康以及新冠肺炎对人类社会影响的理解。该项目将把跨学科的研究成果整合到课程中，并从代表性不足的群体中培养学生。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，2)建立联合学习框架和应用。在第一部分中，该项目将从城市和郊区的学生群体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。该项目将具体分析和确定从手机收集的哪些数据可以作为行为和心理健康的指示和因果因素。在第二部分中，该项目将开发深度学习模型，在严格的隐私保护下，预测人类行为、身心健康状况/趋势随时间的变化。具体地说，预测模型将在联合学习环境中开发，以便在设备上以不同的隐私保证本地训练模型，而不会将传感器数据收集到云中。最后，该项目将在移动电话上开发基于联合学习的行为监控和预测应用程序，并将在从第一个组件开始的研究队列中对原型系统进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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
• 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)
• 作者: Xiaopeng Jiang;Thinh On;Nhathai Phan;Hessamaldin Mohammadi;Vijaya Datta Mayyuri;An M. Chen;Ruoming Jin;C. Borcea

Continual Learning with Differential Privacy

具有差异隐私的持续学习

• DOI: 10.1007/978-3-030-92310-5_39
• 发表时间: 2021
• 期刊: International Conference on Neural Information Processing
• 作者: Desai, Pradnya;Lai, Phung;Phan, NhatHai;Thai, My T.
• 通讯作者: Thai, My T.

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