  In federated learning collaborative learning takes place by a set of clients
who each want to remain in control of how their local training data is used, in
particular, how can each client's local training data remain private?
Differential privacy is one method to limit privacy leakage. We provide a
general overview of its framework and provable properties, adopt the more
recent hypothesis based definition called Gaussian DP or $f$-DP, and discuss
Differentially Private Stochastic Gradient Descent (DP-SGD). We stay at a meta
level and attempt intuitive explanations and insights \textit{in this book
chapter}.
