Samplable Anonymous Aggregation for Private Federated Data Analysis

Learning aggregate population trends can allow for better data-driven decisions, and application of machine learning can improve user experience. Compared to learning from public curated datasets, learning from a larger population offers several benefits. As an example, a next-word prediction model trained on words typed by users (a) can better fit the actual distribution of language used on devices, (b) can adapt faster to shifts in distribution, and (c) can more faithfully represent smaller sub-populations that may not be well-represented in curated datasets. At the same time, training such models may involve sensitive user data.