Predicting Text Selections with Federated Learning

Posted by Florian Hartmann, Software Engineer, Google Research

Smart Text Selection, launched in 2017 as part of Android O, is one of Android’s most frequently used features, helping users select, copy, and use text easily and quickly by predicting the desired word or set of words around a user’s tap, and automatically expanding the selection appropriately. Through this feature, selections are automatically expanded, and for selections with defined classification types, e.g., addresses and phone numbers, users are offered an app with which to open the selection, saving users even more time.

Today we describe how we have improved the performance of Smart Text Selection by using federated learning to train the neural network model on user interactions responsibly while preserving user privacy. This work, which is part of Android’s new Private Compute Core secure environment, enabled us to improve the model’s selection accuracy by up to 20% on some types of entities.

Server-Side Proxy Data for Entity Selections
Smart Text Selection, which is the same technology behind Smart Linkify, does not predict arbitrary selections, but focuses on well-defined entities, such as addresses or phone numbers, and tries to predict the selection bounds for those categories. In the absence of multi-word entities, the model is trained to only select a single word in order to minimize the frequency of making multi-word selections in error.

The Smart Text Selection feature was originally trained using proxy data sourced from web pages to which annotations had been applied. These entities were then embedded in a selection of random text, and the model was trained to select just the entity, without spilling over into the random text surrounding it.

While this approach of training on worked, it had several limitations. The data was quite different from text that we expect users see on-device. For example, websites with annotations typically have

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