CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Networking

职业：具有协作移动边缘网络的无处不在且时间紧迫的联合学习

基本信息

批准号：
2047761
负责人：
Yanmin Gong
金额：
$ 50.9万
依托单位：
University of Texas at San Antonio
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2026-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047761&HistoricalAwards=false
关键词：
CAREER Ubiquitous Time Critical Federated

项目摘要

Federated learning (FL) enables Internet-of-Things (IoT) devices at the network edge to collaboratively learn a shared prediction model while keeping all personal data on the device. However, the current cloud-based FL fails to meet the latency requirements of delay-sensitive IoT applications due to the long-distance transmission between IoT devices and the cloud. This project aims to enable ubiquitous and time-critical FL at the wireless edge to support delay-sensitive and data-driven IoT applications. The project will fulfill the needs of many compelling applications with significant economic and societal impacts such as augmented reality, autonomous driving, mobile healthcare, and smart manufacturing. The project’s educational agenda includes outreach to K-12 with educational summer camps for high-school teachers, mentoring undergraduate and graduate students, especially from minority and underrepresented groups, in the research, and disseminating research outcomes to students and industry partners through new course development and seminars.This project develops a novel Federated learning (FL) framework based on cooperative mobile edge networking that can efficiently support learning and decision making on distributed Internet-of-Things (IoT) data with high accuracy, low latency, and guaranteed privacy. Three interconnected research thrusts are investigated in this project: 1) design of novel network-aware learning algorithms under a two-level network structure to ensure efficient and effective model training from decentralized data on IoT devices over wireless edge networks; 2) jointly optimize resource allocation and learning based on deep reinforcement learning to learn an accurate model rapidly under system heterogeneity and resource constraints; 3) develop novel differential privacy techniques to rigorously protect the privacy of personal data on IoT devices while maintaining high model accuracy and reducing communication cost. The proposed research will enable next-generation wireless edge networks that support a plethora of delay-sensitive and data-driven IoT applications. The proposed research will benefit not only the wireless networking but also machine learning research communities by bridging the gap between the evolving mobile computing and networking technologies and rapidly advancing machine learning techniques.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.

在网络边缘的联合学习（FL）设备可以协作学习该设备上的共享个人数据，但是，由于物联网设备之间的长距离，基于云云在无线边缘上进行了关键的FL，以支持延迟敏感的IoT应用程序包括与K-12的宣传，并为高中生的教师和研究生提供了教育，尤其是来自少数群体和代表性不足的团体的研究生，在研究成果中，通过新的课程开发和研讨会来尝试合作伙伴。在此项目中，支持分布式互联网（IoT）数据的分布式学习和决策。从无线边缘网络上的分散数据的有效培训；物联网设备上的个人数据在维持高模型的同时降低了沟通成本。通过弥合差距，差距是事件和网络技术和迅速发展机器学习技术之间的差距。