  Differential Privacy (DP) is a formal definition of privacy that provides
rigorous guarantees against risks of privacy breaches during data processing.
It makes no assumptions about the knowledge or computational power of
adversaries, and provides an interpretable, quantifiable and composable
formalism. DP has been actively researched during the last 15 years, but it is
still hard to master for many Machine Learning (ML)) practitioners. This paper
aims to provide an overview of the most important ideas, concepts and uses of
DP in ML, with special focus on its intersection with Federated Learning (FL).
