LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning

Oh, S; Park, Jihong; Vepakomma, P; Baek, S; Raskar, R; Bennis, M; Kim, SL

LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning

https://hdl.handle.net/10779/DRO/DU:20589978

conference contribution

posted on 2024-06-06, 01:49 authored by S Oh, Jihong ParkJihong Park, P Vepakomma, S Baek, R Raskar, M Bennis, SL Kim

LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning

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DOI - Is version of https://doi.org/10.1145/3485447.3512153

Location

Virtual Event, Lyon, France

Language

English

Publication classification

E1 Full written paper - refereed

Pagination

3347-3357

Start date

2022-04-25

End date

2022-04-29

ISBN-13

9781450390965

Title of proceedings

WWW 2022 : Proceedings of the Association for Computing Machinery Web Conference 2022

Event

Association for Computing Machinery Web Conference. (31st : 2022 : Virtual Event, Lyon, France)

Publisher

Association for Computing Machinery

Place of publication

New York, N.Y.

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Keywords

Computer Science Computer Science, Cybernetics Computer Science, Software Engineering Computer Science, Theory & Methods Federated Learning Local Parallelism Mixup Scalability Science & Technology Split Learning Technology

LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning

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