Federated Learning for Mobile Keyboard Prediction
This work addresses privacy concerns in mobile keyboard prediction by allowing users to retain control over their data, though it is incremental as it applies an existing federated learning method to this specific domain.
The paper tackled next-word prediction for smartphone keyboards by comparing server-based training with federated learning on client devices, showing that federated learning achieved better prediction recall by enabling training on higher-quality data without exporting sensitive user data.
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. Server-based training using stochastic gradient descent is compared with training on client devices using the Federated Averaging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over the use of their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices.