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Practical Secure Aggregation for Federated Learning on User-Held Data

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arxiv 1611.04482 v1 pith:VZU3UPCR submitted 2016-11-14 cs.CR stat.ML

Keith Bonawitz , Vladimir Ivanov , Ben Kreuter , Antonio Marcedone , H. Brendan McMahan , Sarvar Patel , Daniel Ramage , Aaron Segal
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Karn Seth
This is my paper · ORCID
classification cs.CRstat.ML
keywords aggregationsecuredataprotocolusersvaluesdimensionalexpansion
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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.

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Cited by 22 Pith papers

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