Abstract: Aspects of the present disclosure provide methods and systems for training a neural network. Embodiments include determining a symmetric key that indicates a scrambled ordering of data components. Embodiments include receiving a plurality of training data instances. Embodiments include training a neural network by, for each respective training data instance of the plurality of training data instances: identifying a training input and a training output of the respective training data instance; identifying a plurality of training input components of the training input; providing the plurality of training input components to an input layer of the neural network based on the scrambled ordering indicated by the symmetric key; receiving an output from the neural network in response to the plurality of training input components; and determining whether to modify one or more parameters of the neural network based on the output and the training output of the respective training data instance.

Abstract: Certain aspects of the present disclosure provide methods and systems for training a machine learning model, such as a neural network or deep learning model, in a distributed computing system. In some embodiments, aspects of the machine learning model are trained within containers distributed amongst nodes in the distributed computing environment.