CAREER: Wireless InferNets: Enabling Collaborative Machine Learning Inference on the Network Path
职业:无线推理网:在网络路径上实现协作机器学习推理
基本信息
- 批准号:2044991
- 负责人:
- 金额:$ 50万
- 依托单位:
- 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:2021-03-15 至 2026-02-28
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Machine learning is increasingly being integrated into wireless network applications, such as video surveillance, smart healthcare and industrial internet of things, to derive actionable intelligence from the rich data collected or generated by wireless devices. To support real-time machine learning and decision making in these applications, a large amount of data has to be transferred from the data source to the destination and processed by an inference algorithm in a timely manner. Traditionally, data transfer and machine learning inference are treated as two separate optimization tasks, but such approaches are inefficient as they ignored the interaction between data transfer and machine learning inference, resulting in either a large data transfer delay or a large inference delay. This CAREER project overcomes the limitations of existing approaches by proposing wireless InferNets, a new wireless network architecture that enables collaborative machine learning inference among network nodes on the data transfer path. The successful completion of this CAREER project will promote the understanding of the synergy between distributed inference and networking, and catalyze a paradigm shift of future wireless networks to support emerging applications and services in security, healthcare and other technological domains. The project also contains a significant educational component and provides abundant opportunities to nurture and attract students, especially from underrepresented groups, to engage in computer science and engineering. This CAREER project develops models, algorithms and protocols to realize the core functions of wireless InferNets and address challenges caused by network and device heterogeneity, dynamic and imperfect network states, and multiple user contention for the limited resources via three main research aims. Specifically, it aims at (i) developing practical distributed inference routing algorithms and protocols and conducting theoretical analysis to understand the performance limits; (ii) developing new multi-armed bandit algorithms to perform inference routing with uncertain network information; (iii) developing distributed multi-agent deep reinforcement learning algorithms for inference routing in multi-user wireless InferNets.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.
机器学习越来越多地被集成到无线网络应用中,如视频监控、智能医疗和工业物联网,以从无线设备收集或生成的丰富数据中获取可操作的智能。为了支持这些应用中的实时机器学习和决策,必须将大量数据从数据源传输到目的地,并由推理算法及时处理。传统上,数据传输和机器学习推理被视为两个独立的优化任务,但这种方法效率低下,因为它们忽略了数据传输和机器学习推理之间的相互作用,导致大的数据传输延迟或大的推理延迟。这个CAREER项目通过提出无线Infernets克服了现有方法的局限性,无线Infernets是一种新的无线网络架构,能够在数据传输路径上的网络节点之间进行协作机器学习推理。这个CAREER项目的成功完成将促进对分布式推理和网络之间协同作用的理解,并促进未来无线网络的范式转变,以支持安全、医疗保健和其他技术领域中的新兴应用和服务。该项目还包含一个重要的教育组成部分,并提供大量机会,培养和吸引学生,特别是来自代表性不足的群体的学生,从事计算机科学和工程。该CAREER项目开发模型,算法和协议,以实现无线Infernets的核心功能,并通过三个主要研究目标来解决网络和设备异构性,动态和不完善的网络状态以及多用户争夺有限资源所带来的挑战。具体而言,它的目标是(i)开发实用的分布式推理路由算法和协议,并进行理论分析,以了解性能限制:(ii)开发新的多臂强盗算法,以执行不确定网络信息的推理路由;(iii)开发分布式多智能体深度强化学习算法,用于多智能体中的推理路由,该奖项反映了NSF的法定使命,并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。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Learning the Optimal Partition for Collaborative DNN Training with Privacy Requirements
学习具有隐私要求的协作 DNN 训练的最佳划分
- DOI:10.1109/jiot.2021.3127715
- 发表时间:2021
- 期刊:
- 影响因子:10.6
- 作者:Zhang, Letian;Xu, Jie
- 通讯作者:Xu, Jie
Adversarial Group Linear Bandits and Its Application to Collaborative Edge Inference
- DOI:10.1109/infocom53939.2023.10228900
- 发表时间:2023-05
- 期刊:
- 影响因子:0
- 作者:Yin-Hae Huang;Letian Zhang;J. Xu
- 通讯作者:Yin-Hae Huang;Letian Zhang;J. Xu
Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning
- DOI:10.1145/3442381.3450051
- 发表时间:2021-02
- 期刊:
- 影响因子:0
- 作者:Letian Zhang;Lixing Chen;Jie Xu
- 通讯作者:Letian Zhang;Lixing Chen;Jie Xu
E3Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation
- DOI:10.1145/3576842.3582370
- 发表时间:2023-01
- 期刊:
- 影响因子:0
- 作者:Letian Zhang;Jie Xu
- 通讯作者:Letian Zhang;Jie Xu
On Federated Learning with Energy Harvesting Clients
- DOI:10.1109/icassp43922.2022.9746608
- 发表时间:2022-02
- 期刊:
- 影响因子:0
- 作者:Cong Shen;Jing Yang;Jie Xu
- 通讯作者:Cong Shen;Jing Yang;Jie Xu
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Jie Xu其他文献
Basic principles and optical system design of 17.48 keV high-throughput modified Wolter x-ray microscope
17.48 keV高通量改良Wolter X射线显微镜基本原理及光学系统设计
- DOI:
10.1063/5.0105015 - 发表时间:
2022 - 期刊:
- 影响因子:1.6
- 作者:
Yaran Li;Wenjie Li;Liang Chen;Huanzhen Ma;Xinye Xu;Jie Xu;Xin Wang;Baozhong Mu - 通讯作者:
Baozhong Mu
A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: a case study in Lake Hongze
基于OLCI数据推导内陆浑浊水体粒径分布斜率的半解析算法——以洪泽湖为例
- DOI:
10.1016/j.envpol.2020.116288 - 发表时间:
2020 - 期刊:
- 影响因子:8.9
- 作者:
Shaohua Lei;Jie Xu;Yunmei Li;Lin Li;Heng Lyu;Ge Liu;Yu Chen - 通讯作者:
Yu Chen
Chinese Researchers, Scholarly Communication Behavious, and Trust
中国研究者、学术交流行为和信任
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:2.8
- 作者:
David Nicholas;Jie Xu;Lifang Xu;Jing Su;Anthony Watkinson - 通讯作者:
Anthony Watkinson
Proton-assisted growth of ultra-flat graphene films
质子辅助生长超平坦石墨烯薄膜
- DOI:
10.1038/s41586-019-1870-3 - 发表时间:
2020-01 - 期刊:
- 影响因子:0
- 作者:
Guowen Yuan;Dongjing Lin;Yong Wang;Xianlei Huang;Wang Chen;Xuedong Xie;Junyu Zong;Qian-Qian Yuan;Hang Zheng;Di Wang;Jie Xu;Shao-Chun Li;Yi Zhang;Jian Sun;Xiaoxiang Xi;Libo Gao - 通讯作者:
Libo Gao
A facile and efficient method to improve the selectivity of methyl lactate in the chemocatalytic conversion of glucose catalyzed by homogeneous Lewis acid
一种简便有效的提高均相路易斯酸催化葡萄糖化学催化转化乳酸甲酯选择性的方法
- DOI:
10.1016/j.molcata.2014.01.017 - 发表时间:
2014-07 - 期刊:
- 影响因子:0
- 作者:
Xiaomei Yang;Yunlai Su;Tiangliang Lu;Jie Xu - 通讯作者:
Jie Xu
Jie Xu的其他文献
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{{ truncateString('Jie Xu', 18)}}的其他基金
Elucidating Mechanisms of Metal Sulfide-Enabled Growth of Anoxygenic Photosynthetic Bacteria Using Transcriptomic, Aqueous/Surface Chemical, and Electron Microscopic Tools
使用转录组、水/表面化学和电子显微镜工具阐明金属硫化物促进不产氧光合细菌生长的机制
- 批准号:
2311021 - 财政年份:2023
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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协作研究:CCSS:高密度和重叠的 NextG 无线部署的分层联合学习:编排资源以提高性能
- 批准号:
2319780 - 财政年份:2023
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
SAI-R: Strengthening American Electricity Infrastructure for an Electric Vehicle Future: An Energy Justice Approach
SAI-R:加强美国电力基础设施以实现电动汽车的未来:能源正义方法
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2228603 - 财政年份:2022
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
CCSS: Collaborative Research: Towards a Resource Rationing Framework for Wireless Federated Learning
CCSS:协作研究:无线联邦学习的资源配给框架
- 批准号:
2033681 - 财政年份:2020
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: SWIFT: SMALL: Understanding and Combating Adversarial Spectrum Learning towards Spectrum-Efficient Wireless Networking
合作研究:SWIFT:SMALL:理解和对抗对抗性频谱学习以实现频谱高效的无线网络
- 批准号:
2029858 - 财政年份:2020
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: CNS Core: Small: Towards Automated and QoE-driven Machine Learning Model Selection for Edge Inference
合作研究:CNS 核心:小型:面向边缘推理的自动化和 QoE 驱动的机器学习模型选择
- 批准号:
2006630 - 财政年份:2020
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: Improving Power Grids Weather Resilience through Model-free Dimension Reduction and Stochastic Search for Optimal Hardening
合作研究:通过无模型降维和随机搜索优化强化来提高电网的耐候能力
- 批准号:
1923145 - 财政年份:2019
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: Towards High-Throughput Label-Free Circulating Tumor Cell Separation using 3D Deterministic Dielectrophoresis (D-Cubed)
合作研究:利用 3D 确定性介电泳 (D-Cubed) 实现高通量无标记循环肿瘤细胞分离
- 批准号:
1917295 - 财政年份:2019
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: NSF/ENG/ECCS-BSF: Complex liquid droplet structures as new optical and optomechanical materials
合作研究:NSF/ENG/ECCS-BSF:复杂液滴结构作为新型光学和光机械材料
- 批准号:
1711798 - 财政年份:2017
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
EAGER-Dynamic Data: A New Scalable Paradigm for Optimal Resource Allocation in Dynamic Data Systems via Multi-Scale and Multi-Fidelity Simulation and Optimization
EAGER-动态数据:通过多尺度和多保真度仿真和优化实现动态数据系统中最佳资源分配的新可扩展范式
- 批准号:
1462409 - 财政年份:2015
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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