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Secure Aggregation protocols allow a collection of mutually distrust parties,each holding a private value, to collaboratively compute the sum of thosevalues without revealing the values themselves. We consider training a deepneural network in the Federated Learning model, using distributed stochasticgradient descent across user-held training data on mobile devices, whereinSecure Aggregation protects each user's model gradient. We design a novel,communication-efficient Secure Aggregation protocol for high-dimensional datathat tolerates up to 1/3 users failing to complete the protocol. For 16-bitinput values, our protocol offers 1.73x communication expansion for $2^{10}$users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ usersand $2^{24}$ dimensional vectors.

Practical Secure Aggregation for Federated Learning on User-Held Data