Achieving optimal cache utility in constrained wireless networks through federated learning

Show simple item record Chilukuri, Shanti Pesch, Dirk 2021-02-04T15:49:33Z 2021-02-04T15:49:33Z 2020-08
dc.identifier.citation Chilukuri, S. and Pesch, D. (2020) 'Achieving Optimal Cache Utility in Constrained Wireless Networks through Federated Learning'. 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), Cork, Ireland, 31 Aug -3 Sept, pp. 254-263. doi: 10.1109/WoWMoM49955.2020.00053 en
dc.identifier.startpage 254 en
dc.identifier.endpage 263 en
dc.identifier.isbn 978-1-7281-7374-0
dc.identifier.doi 10.1109/WoWMoM49955.2020.00053 en
dc.description.abstract Edge computing allows constrained end devices in wireless networks to offioad heavy computing tasks or data storage when local resources are insufficient. Edge nodes can provide resources such as the bandwidth, storage and innetwork compute power. For example, edge nodes can provide data caches to which constrained end devices can off-load their data and from where user can access data more effectively. However, fair allocation of these resources to competing end devices and data classes while providing good Quality of Service is a challenging task, due to frequently changing network topology and/or traffic conditions. In this paper, we present Federated learning-based dynamic Cache allocation (FedCache) for edge caches in dynamic, constrained networks. FedCache uses federated learning to learn the benefit of a particular cache allocation with low communication overhead. Edge nodes learn locally to adapt to different network conditions and collaboratively share this knowledge so as to avoid having to transmit all data to a single location. Through this federated learning approach, nodes can find resource allocations that result in maximum fairness or efficiency in terms of the cache hit ratio for a given network state. Simulation results show that cache resource allocation using FedCache results in optimal fairness or efficiency of utility for different classes of data when compared to proportional allocation, while incurring low communication overhead. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers, IEEE en
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. en
dc.subject Edge computing en
dc.subject Fairness en
dc.subject Federated learning en
dc.subject Resource allocation en
dc.subject Resource management en
dc.subject Data models en
dc.subject Quality of service en
dc.subject Silicon en
dc.subject Quality of experience en
dc.subject Bandwidth en
dc.subject Dynamic scheduling en
dc.subject Cache storage en
dc.subject Distributed processing en
dc.subject Learning (artificial intelligence) en
dc.subject Radio networks en
dc.subject Resource allocation en
dc.subject Telecommunication computing en
dc.title Achieving optimal cache utility in constrained wireless networks through federated learning en
dc.type Conference item en
dc.internal.authorcontactother Shanti Chilukuri, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en 2021-02-04T15:40:34Z
dc.description.version Accepted Version en
dc.internal.rssid 553912306
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Horizon 2020 en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Cork, Ireland en
dc.internal.IRISemailaddress en
dc.internal.IRISemailaddress en
dc.relation.project info:eu-repo/grantAgreement/EC/H2020::MSCA-COFUND-FP/713567/EU/Cutting Edge Training - Cutting Edge Technology/EDGE en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/ en

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