# Private Federated Learning in Gboard

arXiv: https://arxiv.org/abs/2306.14793 · HTML: https://mlsanity.com/paper/2306.14793/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2023.

## Built on by (most cited first, 5 of 5)

- [Prompt Public Large Language Models to Synthesize Data for Private On-device Applications](https://mlsanity.com/paper/2404.04360/) (2024)
- [Federated Learning in Practice: Reflections and Projections](https://mlsanity.com/paper/2410.08892/) (2024)
- [A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs](https://mlsanity.com/paper/2408.08868/) (2024)
- [Samplable Anonymous Aggregation for Private Federated Data Analysis](https://mlsanity.com/paper/2307.15017/) (2023)
- [Efficient Language Model Architectures for Differentially Private Federated Learning](https://mlsanity.com/paper/2403.08100/) (2024)

## Abstract (arXiv)

This white paper describes recent advances in Gboard(Google Keyboard)'s use of federated learning, DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm, and secure aggregation techniques to train machine learning (ML) models for suggestion, prediction and correction intelligence from many users' typing data. Gboard's investment in those privacy technologies allows users' typing data to be processed locally on device, to be aggregated as early as possible, and to have strong anonymization and differential privacy where possible. Technical strategies and practices have been established to allow ML models to be trained and deployed with meaningfully formal DP guarantees and high utility. The paper also looks ahead to how technologies such as trusted execution environments may be used to further improve the privacy and security of Gboard's ML models.