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

本文设计了一种新的通信有效的安全聚合协议，适用于在移动设备上跨用户训练数据使用分布式随机梯度下降算法进行联邦学习模型的训练，从而保护每个用户的模型梯度，并且在大规模的高维数据上具有较好的性能表现。