CAREER: Computing-Aware Network Optimization for Efficient Distributed Data Analytics at the Wireless Edge

职业：计算感知网络优化，用于无线边缘的高效分布式数据分析

• 批准号: 2110259
• 负责人: Jia Liu
• 金额: $ 52.41万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110259&HistoricalAwards=false
• 关键词: CAREER; Computing; Aware; Network; Optimization

In recent years, machine learning (ML) and artificial intelligence (AI) applications are quickly finding their ways into our everyday life. All of these applications generate and inject a massive volume of data into the network for a wide range of complex ML/AI data analytics tasks, including but not limited to the training and/or inferences in computer vision, natural language processing, recommendation systems, etc. However, most of the existing wireless network control and optimization algorithms rarely take the new characteristics of ML/AI data analytic traffic into considerations. Likewise, most ML/AI data analytics algorithms oversimplify the underlying wireless networks as "bit pipes" and ignore their complex networking and physical layer constraints, hence leading to poor overall data analytics efficiency. The overarching theme of this CAREER research program is to bridge the gap between the rapidly growing ML/AI data analytics demands and the existing networking and communication technologies. The principal investigator (PI) explore a cross-disciplinary understanding between wireless networking and data analytics through a unified research program, which consists of the development of tractable theoretical models, exploration of theoretical performance bounds and limits, and the development of low-complexity distributed algorithms and protocols that are easy to implement in practice.In this CAREER program, the PI will develop networking-computing co-designs to facilitate ML/AI data analytics with data and model parallelisms in wireless edge networks. The PI will focus on three complementary research thrusts, each of which addresses one key aspect in supporting distributed data analytics at a different protocol layer: (i) communication-efficient distributed optimization at the physical layer; (ii) joint-queueing-computing scheduling at the medium access control layer; and (iii) admission control and resource virtualization at the transport layer. Collectively, the results in this research contribute to a new direction of wireless network control and optimization theory and systems design. The proposed research will serve as a foundation of the next-generation wireless networking that supports a plethora of data analytics and ML/AI applications. Due to its unique scientific and engineering challenges, this research program encompasses strong and holistic expertise in mathematical modeling, optimization, control, queueing theory, stochastic analysis, as well as deep knowledge of ML/AI system operations in practice. The proposed research will support not only the networking, communications, control, and machine learning research communities, but also the general public, by developing new optimization technologies for substantially improved network and data analytics performances.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.

近年来，机器学习（ML）和人工智能（AI）应用程序正在迅速找到他们进入我们日常生活的方式。所有这些应用程序都会在网络中生成并注入大量数据，以完成各种复杂的ML/AI数据分析任务，包括但不限于计算机视觉，自然语言处理，推荐系统等的培训和/或推断。但是，大多数现有的无线网络控制和优化算法很少会非常罕见地对ML/AI Analgorizations进行了ML/AI数据分析的新特征。同样，大多数ML/AI数据分析算法都过度简化了基础无线网络，因为“位管”，忽略了它们复杂的网络和物理层约束，因此导致整体数据分析效率差。该职业研究计划的总体主题是弥合快速增长的ML/AI数据分析需求与现有网络和通信技术之间的差距。首席研究者（PI）通过统一的研究计划探索了无线网络和数据分析之间的跨学科理解，该计划包括开发可访问的理论模型，探索理论绩效界限和限制的理论绩效范围和限制，以及在实践中易于在实践中实施的低复杂性算法和协议的发展，这些算法和协议将易于在实践中实现。 ML/AI数据分析与无线边缘网络中的数据和模型并行性。 PI将重点关注三个互补的研究推力，每个互补的研究推力都介绍了在不同协议层支持分布式数据分析的一个关键方面：（i）物理层处的通信效率高效分布式优化； （ii）中型访问控制层处的联合征收计算计划； （iii）运输层的接收控制和资源虚拟化。总的来说，这项研究的结果为无线网络控制和优化理论和系统设计的新方向做出了贡献。拟议的研究将成为下一代无线网络的基础，该网络支持大量数据分析和ML/AI应用程序。由于其独特的科学和工程挑战，该研究计划涵盖了数学建模，优化，控制，排队理论，随机分析以及实践中ML/AI系统操作的深入了解。拟议的研究不仅将通过开发新的优化技术来实质上改善网络和数据分析性能，因此不仅将支持网络，通信，控制和机器学习研究社区，而且还将支持公众。本奖颁发奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持。

项目成果

Low Sample and Communication Complexities in Decentralized Learning: A Triple Hybrid Approach

• DOI: 10.1109/infocom42981.2021.9488686
• 发表时间: 2021-05
• 期刊: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
• 作者: Xin Zhang;Jia Liu;Zhengyuan Zhu-;E. Bentley

Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning," in Proc. NeurIPS, New Orleans, LA, Dec. 2022

Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning，载于 Proc. NeurIPS，路易斯安那州新奥尔良，2022 年 12 月

• 发表时间: 2022
• 期刊: Proc. NeurIPS
• 作者: Yang, H.;Qiu, P.;Liu, J.
• 通讯作者: Liu, J.

Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach

• DOI: 10.1109/mass52906.2021.00033
• 发表时间: 2021-07
• 期刊: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Fengjiao Li;Jia Liu;Bo Ji

GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning

• DOI: 10.1145/3466772.3467056
• 发表时间: 2021-05
• 期刊: Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
• 作者: Xin Zhang;Jia Liu;Zhengyuan Zhu-;E. Bentley

Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization

用于通信高效分布式优化的自适应多层次符号SGD

• DOI: 10.1109/spawc48557.2020.9154256
• 发表时间: 2020
• 期刊: Special Session on Distributed Signal Processing for Coding and Communications
• 作者: Yang, Haibo;Zhang, Xin;Fang, Minghong;Liu, Jia
• 通讯作者: Liu, Jia