  Deep Learning techniques have achieved remarkable results in many domains.
Often, training deep learning models requires large datasets, which may require
sensitive information to be uploaded to the cloud to accelerate training. To
adequately protect sensitive information, we propose distributed
layer-partitioned training with step-wise activation functions for
privacy-preserving deep learning. Experimental results attest our method to be
simple and effective.
