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Conference Paper: Exploiting Diversity Via Importance-Aware User Scheduling For Fast Edge Learning

TitleExploiting Diversity Via Importance-Aware User Scheduling For Fast Edge Learning
Authors
KeywordsSupport vector machines
Data models
Uncertainty
Training
Distributed databases
Issue Date2020
PublisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome/1001838/all-proceedings
Citation
Proceedings of 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Virtual Conference, Dublin, Ireland, 7-11 June 2020, p. 1-6 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 importance metrics from communication and learning perspectives, i.e., the signal-to-noise ratio (SNR) and data uncertainty. We 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. As demonstrated via experiments using real datasets, the proposed importance-aware scheduling method 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.
DescriptionWS-14:: Workshop on Edge Machine Learning for 5G Mobile Networks and Beyond - Session 3: Machine Learning for Wireless Networks
Persistent Identifierhttp://hdl.handle.net/10722/290712
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLiu, D-
dc.contributor.authorZhu, G-
dc.contributor.authorZhang, J-
dc.contributor.authorHuang, K-
dc.date.accessioned2020-11-02T05:46:03Z-
dc.date.available2020-11-02T05:46:03Z-
dc.date.issued2020-
dc.identifier.citationProceedings of 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Virtual Conference, Dublin, Ireland, 7-11 June 2020, p. 1-6-
dc.identifier.isbn9781728174402-
dc.identifier.issn2474-9133-
dc.identifier.urihttp://hdl.handle.net/10722/290712-
dc.descriptionWS-14:: Workshop on Edge Machine Learning for 5G Mobile Networks and Beyond - Session 3: Machine Learning for Wireless Networks-
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 importance metrics from communication and learning perspectives, i.e., the signal-to-noise ratio (SNR) and data uncertainty. We 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. As demonstrated via experiments using real datasets, the proposed importance-aware scheduling method 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.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome/1001838/all-proceedings-
dc.relation.ispartofIEEE International Conference on Communications Workshops (IICC Workshops) Proceedings-
dc.rightsIEEE International Conference on Communications Workshops (IICC Workshops) Proceedings. Copyright © IEEE.-
dc.rights©2020 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.subjectSupport vector machines-
dc.subjectData models-
dc.subjectUncertainty-
dc.subjectTraining-
dc.subjectDistributed databases-
dc.titleExploiting Diversity Via Importance-Aware User Scheduling For Fast Edge Learning-
dc.typeConference_Paper-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICCWorkshops49005.2020.9145034-
dc.identifier.scopuseid_2-s2.0-85090277739-
dc.identifier.hkuros318007-
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-

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