Pranav Shankar Subramani

Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization

A common pain point in differentially private machine learning is the significant runtime overhead incurred when executing Differentially Private Stochastic Gradient Descent (DPSGD), which may be as large as two orders of magnitude. We thoroughly demonstrate that by exploiting powerful language prim…