# Private federated discovery of out-of-vocabulary words for Gboard

arXiv: https://arxiv.org/abs/2404.11607 · HTML: https://mlsanity.com/paper/2404.11607/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Federated Learning in Practice: Reflections and Projections](https://mlsanity.com/paper/2410.08892/) (2024)
- [Private Frequency Estimation Via Residue Number Systems](https://mlsanity.com/paper/2511.11569/) (2025)

## Abstract (arXiv)

The vocabulary of language models in Gboard, Google's keyboard application, plays a crucial role for improving user experience. One way to improve the vocabulary is to discover frequently typed out-of-vocabulary (OOV) words on user devices. This task requires strong privacy protection due to the sensitive nature of user input data. In this report, we present a private OOV discovery algorithm for Gboard, which builds on recent advances in private federated analytics. The system offers local differential privacy (LDP) guarantees for user contributed words. With anonymous aggregation, the final released result would satisfy central differential privacy guarantees with varepsilon = 0.315, δ = 10-10 for OOV discovery in en-US (English in United States).