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dc.contributor.authorOh, Seungeun
dc.contributor.authorPark, Jihong
dc.contributor.authorVepakomma, Praneeth
dc.contributor.authorBaek, Sihun
dc.contributor.authorRaskar, Ramesh
dc.contributor.authorBennis, Mehdi
dc.contributor.authorKim, Seong-Lyun
dc.date.accessioned2022-11-10T18:11:17Z
dc.date.available2022-11-10T18:11:17Z
dc.date.issued2022-04-25
dc.identifier.isbn978-1-4503-9096-5
dc.identifier.urihttps://hdl.handle.net/1721.1/146330
dc.publisherACM|Proceedings of the ACM Web Conference 2022en_US
dc.relation.isversionofhttps://doi.org/10.1145/3485447.3512153en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceACM|Proceedings of the ACM Web Conference 2022en_US
dc.titleLocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learningen_US
dc.typeArticleen_US
dc.identifier.citationOh, Seungeun, Park, Jihong, Vepakomma, Praneeth, Baek, Sihun, Raskar, Ramesh et al. 2022. "LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning."
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-11-03T01:06:48Z
dc.language.rfc3066en
dc.rights.holderACM
dspace.date.submission2022-11-03T01:06:48Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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