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arXiv · 1809.10678

Introducing Noise in Decentralized Training of Neural Networks

Linara Adilova·Nathalie Paul·Peter Schlicht
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Abstract

It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model. Noise injection in the distributed setup is a straightforward technique and it represents a promising approach to improve the locally trained models. We investigate the effects of noise injection into the neural networks during a decentralized training process. We show both theoretically and empirically that noise injection has no positive effect in expectation on linear models, though. However for non-linear neural networks we empirically show that noise injection substantially improves model quality helping to reach a generalization ability of a local model close to the serial baseline.

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Linara Adilova, Nathalie Paul, Peter Schlicht. 2018-09-27. Introducing Noise in Decentralized Training of Neural Networks. https://arxiv.org/abs/1809.10678

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