Parallelizing Computations of Neural Activations and Layer Normalizations in FHE Environments of Deep Learning Models

원문 (중국어)

Parallelizing functions in deep learning models within homomorphic encryption environments is provided. The method comprises arranging layers in a deep learning model architecture. The layers comprise a first layer computed using a sign function and a second layer having components that can be pre-computed or ignored once computing the sign function on the second layer, wherein the first layer and second layer are adjacent within the deep learning model architecture. The deep learning model architecture is trained with a number of hyper-parameters, and the trained deep learning model architecture is run under homomorphic encryption.