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Federated Learning with Autotuned Communication-Efficient Secure Aggregation

Bonawitz, Keith and Salehi, Fariborz and Konečný, Jakub and McMahan, Brendan and Gruteser, Marco (2019) Federated Learning with Autotuned Communication-Efficient Secure Aggregation. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE , Piscataway, NJ, pp. 1222-1226. ISBN 9781728143002.

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Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user’s device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how random rotation can enable users’ model updates to be quantized much more efficiently, reducing the communication cost between users and the server. Meanwhile, secure aggregation enables the server to learn an aggregate of at least a threshold number of device’s model contributions without observing any individual device’s contribution in unaggregated form. In this paper, we highlight some of the challenges of setting the parameters for secure aggregation to achieve communication efficiency, especially in the context of the aggressively quantized inputs enabled by random rotation. We then develop a recipe for auto-tuning communication-efficient secure aggregation, based on specific properties of random rotation and secure aggregation – namely, the predictable distribution of vector entries post-rotation and the modular wrapping inherent in secure aggregation. We present both theoretical results and initial experiments.

Item Type:Book Section
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Additional Information:© 2019 IEEE.
Record Number:CaltechAUTHORS:20200402-141159842
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102275
Deposited By: Tony Diaz
Deposited On:02 Apr 2020 21:34
Last Modified:16 Nov 2021 18:10

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