  Federated learning (FL) has great potential for large-scale machine learning
(ML) without exposing raw data.Differential privacy (DP) is the de facto
standard of privacy protection with provable guarantees.Advances in ML suggest
that DP would be a perfect fit for FL with comprehensive privacy preservation.
Hence, extensive efforts have been devoted to achieving practically usable FL
with DP, which however is still challenging.Practitioners often not only are
not fully aware of its development and categorization, but also face a hard
choice between privacy and utility. Therefore, it calls for a holistic review
of current advances and an investigation on the challenges and opportunities
for highly usable FL systems with a DP guarantee. In this article, we first
introduce the primary concepts of FL and DP, and highlight the benefits of
integration. We then review the current developments by categorizing different
paradigms and notions. Aiming at usable FL with DP, we present the optimization
principles to seek a better tradeoff between model utility and privacy loss.
Finally, we discuss future challenges in the emergent areas and relevant
research topics.
