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}.

翻译：在联邦学习中，协作学习由一组希望保持对其本地训练数据使用方式控制的客户端参与，特别是：每个客户端的本地训练数据如何保持隐私？差分隐私是限制隐私泄露的一种方法。本文提供了其框架和可证明属性的总体概述，采用基于假设的最新定义，即高斯DP或$f$-DP，并讨论了差分隐私随机梯度下降（DP-SGD）。我们保持在元层面，尝试提供直观的解释和见解《在本章中》。