Search Results for author: Hubert Eichner

Found 7 papers, 4 papers with code

Federated Evaluation of On-device Personalization

no code implementations22 Oct 2019 Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage

Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers.

Language Modelling

Semi-Cyclic Stochastic Gradient Descent

no code implementations23 Apr 2019 Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar

We consider convex SGD updates with a block-cyclic structure, i. e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution.

Federated Learning

Applied Federated Learning: Improving Google Keyboard Query Suggestions

no code implementations7 Dec 2018 Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays

Federated learning is a distributed form of machine learning where both the training data and model training are decentralized.

Federated Learning

Federated Learning for Mobile Keyboard Prediction

4 code implementations8 Nov 2018 Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage

We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones.

Federated Learning Language Modelling

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