SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization

Fuente: arXiv
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Autori principali: Dahan, Tehila, Levy, Kfir Y.
Natura: Preprint
Pubblicazione: 2023
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author Dahan, Tehila
Levy, Kfir Y.
author_facet Dahan, Tehila
Levy, Kfir Y.
contents We consider distributed learning scenarios where M machines interact with a parameter server along several communication rounds in order to minimize a joint objective function. Focusing on the heterogeneous case, where different machines may draw samples from different data-distributions, we design the first local update method that provably benefits over the two most prominent distributed baselines: namely Minibatch-SGD and Local-SGD. Key to our approach is a slow querying technique that we customize to the distributed setting, which in turn enables a better mitigation of the bias caused by local updates.
format Preprint
id arxiv_https___arxiv_org_abs_2304_04169
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
Dahan, Tehila
Levy, Kfir Y.
Machine Learning
Optimization and Control
We consider distributed learning scenarios where M machines interact with a parameter server along several communication rounds in order to minimize a joint objective function. Focusing on the heterogeneous case, where different machines may draw samples from different data-distributions, we design the first local update method that provably benefits over the two most prominent distributed baselines: namely Minibatch-SGD and Local-SGD. Key to our approach is a slow querying technique that we customize to the distributed setting, which in turn enables a better mitigation of the bias caused by local updates.
title SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
topic Machine Learning
Optimization and Control
url https://arxiv.org/abs/2304.04169