Practical Secure Aggregation for Federated Learning on User Held Data
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.
ArXiv