Adilova, LinaraLinaraAdilovaPaul, NathalieNathaliePaulSchlicht, P.P.Schlicht2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/41041910.1007/978-3-030-14880-5_4It 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.en005006629conference paper