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- W2893810361 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." @default.
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- W2893810361 date "2019-01-01" @default.
- W2893810361 modified "2023-10-18" @default.
- W2893810361 title "Introducing Noise in Decentralized Training of Neural Networks" @default.
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- W2893810361 doi "https://doi.org/10.1007/978-3-030-14880-5_4" @default.
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