Practical Secure Aggregation for Federated Learning on User-Held Data

14 Nov 2016Keith BonawitzVladimir IvanovBen KreuterAntonio MarcedoneH. Brendan McMahanSarvar PatelDaniel RamageAaron SegalKarn Seth

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... (read more)

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