Applied Federated Learning: Improving Google Keyboard Query Suggestions
This work addresses enhancing user experience and privacy for keyboard users, but it is incremental as it applies an existing method to a new domain.
The paper tackled improving virtual keyboard search suggestion quality using federated learning in a commercial, global-scale setting, resulting in quality increases without direct access to user data.
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy.