Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization
Yang, Kai1,2,3; Shi, Yuanming1; Ding, Zhi4
AbstractIntelligent mobile platforms such as smart vehicles and drones have recently become the focus of attention for onboard deployment of machine learning mechanisms to enable low latency decisions with low risk of privacy breach. However, most such machine learning algorithms are both computation-and-memory intensive, which makes it highly difficult to implement the requisite computations on a single device of limited computation, memory, and energy resources. Wireless distributed computing presents new opportunities by pooling the computation and storage resources among devices. For low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency, we propose a co-channel communication model and design transceivers by exploiting the locally computed intermediate values as side information. A low-rank optimization model is proposed to maximize the achieved degrees-of-freedom (DoF) by establishing the interference alignment condition for data shuffling. Unfortunately, existing approaches to approximate the rank function fail to yield satisfactory performance due to the poor structure in the formulated low-rank optimization problem. In this paper, we develop an efficient difference-of-convex-functions (DC) algorithm to solve the presented low-rank optimization problem by proposing a novel DC representation for the rank function. Numerical experiments demonstrate that the proposed DC approach can significantly improve the communication efficiency whereas the achievable DoF almost remains unchanged when the number of mobile devices grows.
KeywordWireless distributed computing data shuffling interference alignment low-rank optimization difference-of-convex-functions DC programming Ky Fan 2-k norm
Indexed BySCI ; EI
Funding ProjectNational Science Foundation[CNS-1702752] ; National Science Foundation[ECCS1711823]
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000467582800001
EI Accession Number20192006930946
EI KeywordsDegrees of freedom (mechanics) ; Digital storage ; Distributed computer systems ; Energy resources ; Functions ; Learning algorithms ; Machine learning ; Optimization
EI Classification NumberEnergy Resources and Renewable Energy Issues:525.1 ; Radio Systems and Equipment:716.3 ; Data Storage, Equipment and Techniques:722.1 ; Digital Computers and Systems:722.4 ; Mathematics:921 ; Optimization Techniques:921.5 ; Mechanics:931.1
Original Document TypeArticle
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Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorShi, Yuanming
Affiliation1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Univ Calif Davis, Dept Elect & Comp Engn, Davis, CA 95616 USA
First Author AffilicationSchool of Information Science and Technology
Corresponding Author AffilicationSchool of Information Science and Technology
First Signature AffilicationSchool of Information Science and Technology
Recommended Citation
GB/T 7714
Yang, Kai,Shi, Yuanming,Ding, Zhi. Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING,2019,67(12):3087-3099.
APA Yang, Kai,Shi, Yuanming,&Ding, Zhi.(2019).Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization.IEEE TRANSACTIONS ON SIGNAL PROCESSING,67(12),3087-3099.
MLA Yang, Kai,et al."Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization".IEEE TRANSACTIONS ON SIGNAL PROCESSING 67.12(2019):3087-3099.
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