@inproceedings{xu-etal-2023-federated,
    title = "Federated Learning of Gboard Language Models with Differential Privacy",
    author = "Xu, Zheng  and
      Zhang, Yanxiang  and
      Andrew, Galen  and
      Choquette, Christopher  and
      Kairouz, Peter  and
      Mcmahan, Brendan  and
      Rosenstock, Jesse  and
      Zhang, Yuanbo",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.60",
    doi = "10.18653/v1/2023.acl-industry.60",
    pages = "629--639",
    abstract = "We train and deploy language models (LMs) with federated learning (FL) and differential privacy (DP) in Google Keyboard (Gboard). The recent DP-Follow the Regularized Leader (DP-FTRL) algorithm is applied to achieve meaningfully formal DP guarantees without requiring uniform sampling of clients. To provide favorable privacy-utility trade-offs, we introduce a new client participation criterion and discuss the implication of its configuration in large scale systems. We show how quantile-based clip estimation can be combined with DP-FTRL to adaptively choose the clip norm during training or reduce the hyperparameter tuning in preparation of training. With the help of pretraining on public data, we trained and deployed more than fifteen Gboard LMs that achieve high utility and {\$}{\textbackslash}rho-{\$}zCDP privacy guarantees with {\$}{\textbackslash}rho {\textbackslash}in (0.3, 2){\$}, with one model additionally trained with secure aggregation. We summarize our experience and provide concrete suggestions on DP training for practitioners.",
}

We train and deploy language models (LMs) with federated learning (FL) and differential privacy (DP) in Google Keyboard (Gboard). The recent DP-Follow the Regularized Leader (DP-FTRL) algorithm is applied to achieve meaningfully formal DP guarantees without requiring uniform sampling of clients. To provide favorable privacy-utility trade-offs, we introduce a new client participation criterion and discuss the implication of its configuration in large scale systems. We show how quantile-based clip estimation can be combined with DP-FTRL to adaptively choose the clip norm during training or reduce the hyperparameter tuning in preparation of training. With the help of pretraining on public data, we trained and deployed more than fifteen Gboard LMs that achieve high utility and {\$}{\textbackslash}rho-{\$}zCDP privacy guarantees with {\$}{\textbackslash}rho {\textbackslash}in (0.3, 2){\$}, with one model additionally trained with secure aggregation. We summarize our experience and provide concrete suggestions on DP training for practitioners.
Xu, Zheng Zhang, Yanxiang Andrew, Galen Choquette, Christopher Kairouz, Peter Mcmahan, Brendan Rosenstock, Jesse Zhang, Yuanbo
Federated Learning of Gboard Language Models with Differential Privacy