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，协作学习由一组客户端共同完成，每个客户端都希望掌控其本地训练数据的使用方式，特别是如何确保每个客户端的本地训练数据保持隐私？差分隐私是限制隐私泄露的一种方法。我们概述了其框架和可证明的性质，采用了基于假设的最新定义——高斯DP或$f$-DP，并讨论了差分隐私随机梯度下降（DP-SGD）。我们保持元层面的讨论，并试图提供直观的解释和见解（本章内容）。