Federated adaptive asynchronous clustering algorithm for Wireless Mesh Networks

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dc.contributor.authorQiao, Cheng
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorZhang, Fan
dc.contributor.authorTian, Zhihong
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderGuangdong Science and Technology Departmenten
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderDepartment of Education of Guangdong Provinceen
dc.date.accessioned2021-10-20T09:54:18Z
dc.date.available2021-10-20T09:54:18Z
dc.date.issued2021-10-14
dc.date.updated2021-10-20T09:20:27Z
dc.description.abstractIt is a challenge to generate an accurate machine learning model in a distributed network due to the increased concern in data privacy and high cost in gathering all raw data. This paper presents an adaptive asynchronous distributed clustering algorithm for agents in wireless network to learn the global models, while the privacy is protected. Moreover, the communication cost and clustering quality can be adaptively balanced. The proposed clustering algorithm does not require the number of clusters to be pre-defined. To improve the accuracy of the global model, we propose a bounding boxes based method to fully utilize the shape information of clusters. In addition, we consider different knowledge levels of agent and different requirements about the global model. In experiments on randomly generated network topologies, we demonstrate that methods which do more extensive clustering in each cycle, and which exchange descriptions of cluster shape and density instead of just centroids and data counts, achieve more consistent clustering, in significantly shorter elapsed time. We also show that the proposed methods can learn the same number of clusters as the ground truth when clusters are well separated from each other.en
dc.description.sponsorshipNational Natural Science Foundation of China (Grant Number: U20B2046); Guangdong Science and Technology Department (Guangdong Province Key Area R&D Program of China under Grant No.2019B010137004) Science Foundation Ireland (Grant Number SFI/12/RC/2289-P2;16/SP/3804); Department of Education of Guangdong Province (Universities and Colleges Pearl River Scholar Funded Scheme 2019)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationQiao, C., Brown, K. N., Zhang, F. and Tian, Z. (2021) 'Federated adaptive asynchronous clustering algorithm for Wireless Mesh Networks', IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109/TKDE.2021.3119550en
dc.identifier.doi10.1109/TKDE.2021.3119550en
dc.identifier.eissn1558-2191
dc.identifier.issn1041-4347
dc.identifier.journaltitleIEEE Transactions on Knowledge and Data Engineeringen
dc.identifier.urihttps://hdl.handle.net/10468/12113
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.rights© 2021, 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.en
dc.subjectDistributed algorithmen
dc.subjectAsynchronousen
dc.subjectClustering algorithmen
dc.subjectWireless Mesh Networken
dc.titleFederated adaptive asynchronous clustering algorithm for Wireless Mesh Networksen
dc.typeArticle (peer-reviewed)en
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