Search Results for author: Andrew Hard

Found 7 papers, 2 papers with code

Mixed Federated Learning: Joint Decentralized and Centralized Learning

no code implementations26 May 2022 Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews

For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution.

Federated Learning

Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift

no code implementations23 Nov 2021 Sean Augenstein, Andrew Hard, Kurt Partridge, Rajiv Mathews

With privacy as a motivation, Federated Learning (FL) is an increasingly used paradigm where learning takes place collectively on edge devices, each with a cache of user-generated training examples that remain resident on the local device.

Federated Learning

Training Keyword Spotting Models on Non-IID Data with Federated Learning

no code implementations21 May 2020 Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio Lopez Moreno, Rajiv Mathews

We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a centrally-trained model.

Data Augmentation Federated Learning +1

Federated Learning for Mobile Keyboard Prediction

5 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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