CAREER: Memory-Efficient, Heterogeneity-Aware and Robust Architecture for Federated Intelligence on Edge Devices

职业：边缘设备上联邦智能的内存高效、异构感知和鲁棒架构

• 批准号: 2044841
• 负责人: Cong Wang
• 金额: $ 47万
• 依托单位: Old Dominion University Research Foundation
• 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: 2021-03-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044841&HistoricalAwards=false
• 关键词: CAREER; Memory; Efficient; Heterogeneity; Aware

The recent trend of migrating computation from the centralized cloud to distributed edge devices is reshaping the landscape of today’s Internet, especially under the unprecedented challenges of the COVID19 pandemic. With privacy being a critical concern in data aggregation, Federated Learning emerges as a promising solution to such privacy-utility challenge. It pushes the computation towards consumer’s edge devices, where the data is generated. By exchanging statistical information, the participants perform collaborative learning in a distributed fashion. Unfortunately, the original design still faces new system-architectural challenges from limited memory, software/hardware heterogeneity, security and statistical diversity from different edge devices. The overarching goal of this CAREER project is to design, optimize and implement a memory-efficient, heterogeneity-aware and robust architecture for federated learning on consumer’s edge devices. In particular, it aims to: 1) remove the memory barriers of running the computational-intensive learning tasks; 2) resolve the software and hardware heterogeneity among various kinds of devices; 3) secure the information exchange and the machine learning backend. The research will provide a stack of solutions to address the urging needs in realizing collaborative intelligence on edge devices with computation/memory/energy-efficiencies, security and robustness. This research will address an urgent problem to bridge the gap between the vast data available from consumer’s edge devices and the rising interest of utilizing such private data to improve our wellbeing. The algorithms and tools developed in this CAREER project will lay the foundations to a plethora of new applications on massively distributed edge devices, as the essential elements for building a smart, connected and resilient community. The CAREER program will advance STEM education by developing new educational components related to machine learning, edge computing and security. This includes diverse outreach plans of cybersecurity summer camps, junior research symposium, high school instructor mentorship, coding competitions and the inclusion of underrepresented minority and women engineers. The potential use cases will be also explored with the collaborating industrial partners to enrich their business models.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.

最近将计算从集中式云迁移到分布式边缘设备的趋势正在重塑当今互联网的格局，特别是在COVID 19大流行的前所未有的挑战下。随着隐私成为数据聚合中的一个关键问题，联邦学习成为解决这种隐私效用挑战的一个有前途的解决方案。它将计算推向消费者的边缘设备，在那里生成数据。通过交换统计信息，参与者以分布式方式进行协作学习。不幸的是，最初的设计仍然面临着来自有限内存、软件/硬件异构性、安全性和不同边缘设备的统计多样性的新系统架构挑战。这个CAREER项目的首要目标是设计、优化和实现一个内存高效、异构感知和健壮的架构，用于在消费者的边缘设备上进行联合学习。具体而言，其目的是：1）消除运行计算密集型学习任务的内存障碍; 2）解决各种设备之间的软件和硬件异构性; 3）保护信息交换和机器学习后端。该研究将提供一系列解决方案，以满足在具有计算/内存/能源效率，安全性和鲁棒性的边缘设备上实现协作智能的迫切需求。这项研究将解决一个紧迫的问题，即弥合消费者边缘设备提供的大量数据与利用这些私人数据改善我们福祉的日益增长的兴趣之间的差距。在这个CAREER项目中开发的算法和工具将为大规模分布式边缘设备上的大量新应用奠定基础，作为构建智能，连接和弹性社区的基本要素。CAREER计划将通过开发与机器学习，边缘计算和安全相关的新教育组件来推进STEM教育。这包括网络安全夏令营、初级研究研讨会、高中教师辅导、编码竞赛等各种外联计划，以及将代表性不足的少数民族和女工程师纳入其中。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。