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

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Publication date: 9 April 2023

Abstract: 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.

Has companion code repository: https://github.com/dahan198/slowcal-sgd

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