CAREER: Communication- Efficient Distributed Computation: Information- Theoretic Foundations and Algorithms

职业：通信高效分布式计算：信息理论基础和算法

• 批准号: 1651492
• 负责人: Ravi Tandon
• 金额: $ 50万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651492&HistoricalAwards=false
• 关键词: CAREER; Communication; Efficient; Distributed; Computation

This CAREER project is motivated by the exponential rise in the volume of data being processed for knowledge discovery applications, such as business process optimization, healthcare analytics, cybersecurity, and scientific computing. To speed up computation, there is an increasing interest in distributed computation; however, distributing a computational task over multiple heterogeneous machines involves data movement between machines leading to communication bottlenecks, significantly impacting the time for computation. By reducing the cost and time for distributed computation, the research outcomes can have a significant and immediate economic benefit as well as a lasting societal impact. The research objectives of this project are strongly interconnected with educational components and outreach activities, which include undergraduate research involvement, interdisciplinary curriculum development, outreach to K-12 in collaboration with SARSEF (Southern Arizona Research, Science and Engineering Foundation), and raising STEM awareness through the STEM learning center and the Arizona Science, Engineering, and Mathematics (ASEM) scholars mentoring program.To enable communication-efficient distributed computation, this CAREER project will pursue the following intertwined research objectives. The first research objective is to make distributed data shuffling communication-efficient through novel coded data delivery mechanisms, that are adaptable to the underlying topology and can cope with the temporal nature of computational tasks. The second research objective is to reduce the impact of computational heterogeneity by novel work exchange algorithms through intermediate communication while minimizing the time for computation. The third research objective complements the first two in characterizing fundamental information theoretic limits by developing lower bounds on the communication necessary for: a) data shuffling as a function of storage and topology; and b) the minimum time for computation as a function of intermediate communication and storage.

这个CAREER项目的动机是为知识发现应用程序处理的数据量呈指数级增长，例如业务流程优化，医疗保健分析，网络安全和科学计算。为了加快计算速度，人们对分布式计算越来越感兴趣;然而，在多个异构机器上分配计算任务涉及机器之间的数据移动，导致通信瓶颈，显著影响计算时间。通过减少分布式计算的成本和时间，研究成果可以产生显着和直接的经济效益以及持久的社会影响。该项目的研究目标与教育组成部分和推广活动密切相关，包括本科生参与研究，跨学科课程开发，与SARSEF合作推广到K-12（南亚利桑那州研究，科学和工程基金会），并通过STEM学习中心和亚利桑那州科学，工程，为了实现高效的分布式计算，这个CAREER项目将追求以下相互交织的研究目标。第一个研究目标是通过新的编码数据传输机制，使分布式数据洗牌通信效率，这是适应底层拓扑结构，可以科普计算任务的时间性质。第二个研究目标是减少计算异构性的影响，通过新的工作交换算法，通过中间通信，同时最大限度地减少计算时间。第三个研究目标补充了前两个特征的基本信息理论的限制，开发下限的通信所需的：a）数据洗牌作为一个功能的存储和拓扑结构;和B）作为一个功能的中间通信和存储的计算时间最短。

项目成果

Context-Aware Local Information Privacy

• DOI: 10.1109/tifs.2021.3087350
• 发表时间: 2021-01-01
• 期刊: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
• 影响因子: 6.8
• 作者: Jiang, Bo;Seif, Mohamed;Li, Ming
• 通讯作者: Li, Ming

Generalization Bounds for Neural Belief Propagation Decoders

• DOI: 10.1109/isit54713.2023.10206901
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT)
• 作者: S. Adiga;Xin Xiao;Ravi Tandon;Bane V. Vasic;Tamal Bose

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

• DOI: 10.1109/jsac.2022.3142358
• 发表时间: 2021-07
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: S. Ulukus;S. Avestimehr;M. Gastpar;S. Jafar;R. Tandon;Chao Tian

On the Capacity of Latent Variable Private Information Retrieval

论潜变量隐私信息检索能力

• DOI: 10.1109/isit45174.2021.9517754
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Information Theory (ISIT
• 作者: Samy, Islam;Attia, Mohamed A.;Tandon, Ravi;Lazos, Loukas
• 通讯作者: Lazos, Loukas

Asymmetric Leaky Private Information Retrieval

• DOI: 10.1109/tit.2021.3085363
• 发表时间: 2020-06
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: I. Samy;M. Attia;R. Tandon;Loukas Lazos