  Federated learning (FL) has emerged as a promising collaboration paradigm by
enabling a multitude of parties to construct a joint model without exposing
their private training data. Three main challenges in FL are efficiency,
privacy, and robustness. The recently proposed SIGNSGD with majority vote shows
a promising direction to deal with efficiency and Byzantine robustness.
However, there is no guarantee that SIGNSGD is privacy-preserving. In this
paper, we bridge this gap by presenting an improved method called DP-SIGNSGD,
which can meet all the aforementioned properties. We further propose an
error-feedback variant of DP-SIGNSGD to improve accuracy. Experimental results
on benchmark image datasets demonstrate the effectiveness of our proposed
methods.
