Adaptive Deadline Determination for Mobile Device Selection in Federated Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaewook | - |
dc.contributor.author | Ko, Haneul | - |
dc.contributor.author | Pack, Sangheon | - |
dc.date.accessioned | 2022-04-18T08:42:22Z | - |
dc.date.available | 2022-04-18T08:42:22Z | - |
dc.date.created | 2022-04-18 | - |
dc.date.issued | 2022-03 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/140277 | - |
dc.description.abstract | Owing to dynamically changing resources and channel conditions of mobile devices (MDs), when a static deadline-based MD selection scheme is used for federated learning, resource utilization of MDs can be degraded. To mitigate this problem, we propose an adaptive deadline determination (ADD) algorithm for MD selection, where a deadline for each round is adaptively determined with the consideration of the performance disparity of MDs. Evaluation results demonstrate that ADD can achieve the fastest average convergence time among the comparison schemes. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | CLIENT SELECTION | - |
dc.title | Adaptive Deadline Determination for Mobile Device Selection in Federated Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Pack, Sangheon | - |
dc.identifier.doi | 10.1109/TVT.2021.3136308 | - |
dc.identifier.scopusid | 2-s2.0-85121803344 | - |
dc.identifier.wosid | 000769985100100 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.71, no.3, pp.3367 - 3371 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.title | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.volume | 71 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 3367 | - |
dc.citation.endPage | 3371 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.subject.keywordPlus | CLIENT SELECTION | - |
dc.subject.keywordAuthor | Servers | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Convergence | - |
dc.subject.keywordAuthor | Adaptation models | - |
dc.subject.keywordAuthor | Mobile handsets | - |
dc.subject.keywordAuthor | Federated learning | - |
dc.subject.keywordAuthor | mobile device selection | - |
dc.subject.keywordAuthor | adaptive deadline | - |
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