Sparsity-Preserving Differentially Private Training of Large Embedding
  Models
Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang
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.