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

• 批准号: 2044991
• 负责人: Jie Xu
• 金额: $ 50万
• 依托单位: University of Miami
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044991&HistoricalAwards=false
• 关键词: CAREER; Wireless; InferNets; Enabling; Collaborative

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集成到无线网络应用程序中，例如视频监视，智能医疗保健和工业互联网，以从无线设备收集或生成的丰富数据中得出可行的智能。为了支持这些应用程序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，并通过推理算法及时处理。传统上，数据传输和机器学习推论被视为两个独立的优化任务，但是这种方法忽略了数据传输和机器学习推断之间的相互作用，从而导致大数据传输延迟或大推断延迟。该职业项目通过提出无线地狱的局限性，这是一个新的无线网络体系结构，可以使网络节点之间的协作机器学习推断在数据传输路径上。该职业项目的成功完成将促进对分布式推理和网络之间的协同作用的理解，并催化未来无线网络的范式转移，以支持安全，医疗保健和其他技术领域中新兴应用和服务。该项目还包含重要的教育组成部分，并为培育和吸引学生尤其是代表性不足的团体的学生提供了丰富的机会，从而从事计算机科学和工程学。该职业项目开发了模型，算法和协议，以实现无线地狱的核心功能，并解决由网络和设备异质性，动态和不完美的网络状态以及通过三个主要研究目标对有限资源的多个用户争论引起的挑战。具体而言，它旨在（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使用不确定的网络信息执行推理路由； （iii）开发用于多用户无线地狱中推理路由的分布式多代理深入学习算法。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得支持的。

项目成果

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: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Zhang, Letian;Xu, Jie
• 通讯作者: Xu, Jie

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: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
• 作者: Letian Zhang;Jie Xu

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: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 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: Proceedings of the Web Conference 2021
• 作者: Letian Zhang;Lixing Chen;Jie Xu

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

• DOI: 10.1109/twc.2021.3113346
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Jie Xu;Heqiang Wang;Lixing Chen