Chapter 2: Considerations on the theory of training models with differential privacy
Marten van Dijka,b; Phuong Ha Nguyenc aCWI, Amsterdam, NetherlandsbVU University Amsterdam, Amsterdam, NetherlandsceBay Inc., San Jose, CA, United States
Abstract
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 each client's local training data can 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). ...
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