FedQOGD: Federated Quantized Online Gradient Descent with Distributed Time-Series Data
DC Field | Value | Language |
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dc.contributor.author | Park, Jonghwan | - |
dc.contributor.author | Kwon, Dohyeok | - |
dc.contributor.author | Hong, Songnam | - |
dc.date.accessioned | 2022-07-06T04:08:19Z | - |
dc.date.available | 2022-07-06T04:08:19Z | - |
dc.date.created | 2022-06-29 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 1525-3511 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138739 | - |
dc.description.abstract | We investigate an online federated learning (in short, OFL), in which many edge nodes receive their own time-series data and train a sequence of global models under the orchestration of a central server while keeping data localized. In this framework, we propose a communication efficient federated quantized online gradient descent (FedQOGD) by means of a stochastic quantization and partial node participation. We theoretically prove that FedQOGD over T time slots can achieve an optimal sublinear regret bound {mathcal{O}(sqrt T ) for any quantization level (e.g., 1-level quantization), even when every node can participate in a learning process sporadically. Our analysis reveals that FedQOGD yields the same asymptotic performance as the centralized counterpart (i.e., all local data are gathered at the central server) while having a low-communication overhead and preserving an edge-node privacy. Finally, we verify the effectiveness of our algorithm via experiments with a real-world MNIST dataset on online classification task. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | FedQOGD: Federated Quantized Online Gradient Descent with Distributed Time-Series Data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Songnam | - |
dc.identifier.doi | 10.1109/WCNC51071.2022.9771579 | - |
dc.identifier.scopusid | 2-s2.0-85130724270 | - |
dc.identifier.wosid | 000819473100092 | - |
dc.identifier.bibliographicCitation | IEEE Wireless Communications and Networking Conference, WCNC, v.2022-April, pp.536 - 541 | - |
dc.relation.isPartOf | IEEE Wireless Communications and Networking Conference, WCNC | - |
dc.citation.title | IEEE Wireless Communications and Networking Conference, WCNC | - |
dc.citation.volume | 2022-April | - |
dc.citation.startPage | 536 | - |
dc.citation.endPage | 541 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | distributed learning | - |
dc.subject.keywordAuthor | Federated learning | - |
dc.subject.keywordAuthor | online learning | - |
dc.subject.keywordAuthor | regret analysis | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9771579 | - |
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