Applied Federated Learning: Improving Google Keyboard Query Suggestions

Yang, Timothy, Andrew, Galen, Eichner, Hubert, Sun, Haicheng, Li, Wei, Kong, Nicholas, Ramage, Daniel, Beaufays, Françoise

arXiv.org Machine Learning 

In contrast to traditional server-side training whereuser data is aggregated on centralized servers for training, FL instead trains models on end user devices while aggregating only ephemeral parameter updates on a centralized server.This is particularly advantageous for environments whereprivacy is paramount. The Google Keyboard (Gboard) is a virtual keyboard for mobile devices with over 1 billion installs in 2018. Gboard includes both typing features like text autocorrection, nextword predictionand word completions as well as expression features like emoji, GIFs and Stickers (curated, expressive illustrations andanimations). As both a mobile application and keyboard, Gboard has unique constraints which lends itself well to both on-device inference and training. First, as a keyboard applicationwith access to much of what a user types into their mobile device, Gboard must respect the user's privacy.

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