Distributed Online Learning With Multiple Kernels
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Songnam | - |
dc.contributor.author | Chae, Jeongmin | - |
dc.date.accessioned | 2023-05-03T14:27:51Z | - |
dc.date.available | 2023-05-03T14:27:51Z | - |
dc.date.created | 2021-11-22 | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185441 | - |
dc.description.abstract | We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion Online learning is additionally assumed where every learner receives continuous streaming data locally This learning model is called a fully distributed online learning or a fully decentralized online federated learning). For this model, we propose a novel learning framework with multiple kernels, which is named DOMKL. The proposed DOMKL is devised by harnessing the principles of an online alternating direction method of multipliers and a distributed Hedge algorithm. We theoretically prove that DOMKL over T time slots can achieve an optimal sublinear regret O(√T), implying that every learner in the network can learn a common function having a diminishing gap from the best function in hindsight. Our analysis also reveals that DOMKL yields the same asymptotic performance as the state-of-the-art centralized approach while keeping local data at edge learners. Via numerical tests with real datasets, we demonstrate the effectiveness of the proposed DOMKL on various online regression and time-series prediction tasks. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Distributed Online Learning With Multiple Kernels | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Songnam | - |
dc.identifier.doi | 10.1109/TNNLS.2021.3105146 | - |
dc.identifier.scopusid | 2-s2.0-85113855147 | - |
dc.identifier.wosid | 000732311800001 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.34, no.3, pp.1263 - 1277 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.volume | 34 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1263 | - |
dc.citation.endPage | 1277 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | CHALLENGES | - |
dc.subject.keywordPlus | INTERNET | - |
dc.subject.keywordPlus | THINGS | - |
dc.subject.keywordAuthor | Decentralized federated learning | - |
dc.subject.keywordAuthor | distributed online learning | - |
dc.subject.keywordAuthor | multiple kernel learning (MKL) | - |
dc.subject.keywordAuthor | online learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9520655 | - |
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