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Why (and When) does Local SGD Generalize Better than SGD?

ICLR 2023 · Optimization › Implicit bias and generalization · approach: SGD noise and flat minima · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add local steps with H=750SGD-pretrained model · CIFAR-10test accuracy: +1.1%Helped
Add local steps with H=26SGD-pretrained model · ImageNettest accuracy: +0.3%Helped
Use a small learning rate (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyHelped
Use a large learning rate (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyHurt
Train for sufficient time (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyHelped
Use insufficient training time (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyHurt
Remove SGD pretraining and start from random initialization (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyNo clear effect
Remove Nesterov momentum and learning-rate decay (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyNo clear effect
Tune H according to the learning rate (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyHelped
Increase H beyond its useful range (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyMixed
Increase H from 24 to 900 after learning-rate decay (ablation)Local SGD · CIFAR-10test accuracy: 24 to 900Helped
Increase worker count K while keeping η and H fixed (ablation)Local SGD · CIFAR-10 and ImageNettest accuracyHelped

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