  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.
