  As the use of large embedding models in recommendation systems and language
applications increases, concerns over user data privacy have also risen.
DP-SGD, a training algorithm that combines differential privacy with stochastic
gradient descent, has been the workhorse in protecting user privacy without
compromising model accuracy by much. However, applying DP-SGD naively to
embedding models can destroy gradient sparsity, leading to reduced training
efficiency. To address this issue, we present two new algorithms, DP-FEST and
DP-AdaFEST, that preserve gradient sparsity during private training of large
embedding models. Our algorithms achieve substantial reductions ($10^6 \times$)
in gradient size, while maintaining comparable levels of accuracy, on benchmark
real-world datasets.
