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Article: Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning

TitleData-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning
Authors
KeywordsData models
Scheduling
Computational modeling
Wireless communication
Training
Issue Date2021
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6687307
Citation
IEEE Transactions on Cognitive Communications and Networking, 2021, v. 7 n. 1, p. 265-278 How to Cite?
AbstractWith the prevalence of intelligent mobile applications, edge learning is emerging as a promising technology for powering fast intelligence acquisition for edge devices from distributed data generated at the network edge. One critical task of edge learning is to efficiently utilize the limited radio resource to acquire data samples for model training at an edge server. In this paper, we develop a novel user scheduling algorithm for data acquisition in edge learning, called (data) importance-aware scheduling. A key feature of this scheduling algorithm is that it takes into account the informativeness of data samples, besides communication reliability. Specifically, the scheduling decision is based on a data importance indicator (DII), elegantly incorporating two 'important' metrics from communication and learning perspectives, i.e., the signal-to-noise ratio (SNR) and data uncertainty. We first derive an explicit expression for this indicator targeting the classic classifier of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. Then, the result is extended to convolutional neural networks (CNN) by replacing the distance based uncertainty measure with the entropy. As demonstrated via experiments using real datasets, the proposed importance-aware scheduling can exploit the two-fold multi-user diversity, namely the diversity in both the multiuser channels and the distributed data samples. This leads to faster model convergence than the conventional scheduling schemes that exploit only a single type of diversity.
Persistent Identifierhttp://hdl.handle.net/10722/295849
ISSN
2023 Impact Factor: 7.4
2023 SCImago Journal Rankings: 3.371
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLIU, D-
dc.contributor.authorZHU, G-
dc.contributor.authorZhang, J-
dc.contributor.authorHuang, K-
dc.date.accessioned2021-02-08T08:14:54Z-
dc.date.available2021-02-08T08:14:54Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Cognitive Communications and Networking, 2021, v. 7 n. 1, p. 265-278-
dc.identifier.issn2332-7731-
dc.identifier.urihttp://hdl.handle.net/10722/295849-
dc.description.abstractWith the prevalence of intelligent mobile applications, edge learning is emerging as a promising technology for powering fast intelligence acquisition for edge devices from distributed data generated at the network edge. One critical task of edge learning is to efficiently utilize the limited radio resource to acquire data samples for model training at an edge server. In this paper, we develop a novel user scheduling algorithm for data acquisition in edge learning, called (data) importance-aware scheduling. A key feature of this scheduling algorithm is that it takes into account the informativeness of data samples, besides communication reliability. Specifically, the scheduling decision is based on a data importance indicator (DII), elegantly incorporating two 'important' metrics from communication and learning perspectives, i.e., the signal-to-noise ratio (SNR) and data uncertainty. We first derive an explicit expression for this indicator targeting the classic classifier of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. Then, the result is extended to convolutional neural networks (CNN) by replacing the distance based uncertainty measure with the entropy. As demonstrated via experiments using real datasets, the proposed importance-aware scheduling can exploit the two-fold multi-user diversity, namely the diversity in both the multiuser channels and the distributed data samples. This leads to faster model convergence than the conventional scheduling schemes that exploit only a single type of diversity.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6687307-
dc.relation.ispartofIEEE Transactions on Cognitive Communications and Networking-
dc.rightsIEEE Transactions on Cognitive Communications and Networking. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectData models-
dc.subjectScheduling-
dc.subjectComputational modeling-
dc.subjectWireless communication-
dc.subjectTraining-
dc.titleData-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning-
dc.typeArticle-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TCCN.2020.2999606-
dc.identifier.scopuseid_2-s2.0-85096315566-
dc.identifier.hkuros321243-
dc.identifier.volume7-
dc.identifier.issue1-
dc.identifier.spage265-
dc.identifier.epage278-
dc.identifier.isiWOS:000626515700022-
dc.publisher.placeUnited States-

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