Poster: Maintaining Training Efficiency and Accuracy for Edge-assisted Online Federated Learning with ABS

Jiayu Wang, Zehua Guo, Sen Liu, Yuanqing Xia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper proposes Adaptive Batch Sizing (ABS) for online federated learning. ABS is an iteration process-efficient solution that adaptively adjusts batch size of the training process at edge nodes. Preliminary results show that ABS maintains training efficiency and accuracy, compared with existing iteration round-efficient solutions.

Original languageEnglish
Title of host publication28th IEEE International Conference on Network Protocols, ICNP 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728169927
DOIs
Publication statusPublished - 13 Oct 2020
Event28th IEEE International Conference on Network Protocols, ICNP 2020 - Madrid, Spain
Duration: 13 Oct 202016 Oct 2020

Publication series

NameProceedings - International Conference on Network Protocols, ICNP
Volume2020-October
ISSN (Print)1092-1648

Conference

Conference28th IEEE International Conference on Network Protocols, ICNP 2020
Country/TerritorySpain
CityMadrid
Period13/10/2016/10/20

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