Papers with DP-FTRL
Federated Learning of Gboard Language Models with Differential Privacy (2023.acl-industry)
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Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, Yuanbo Zhang
| Challenge: | Using federated learning and differential privacy, we train and deploy language models with federation and DP in Google Keyboard. |
| Approach: | They train and deploy language models with federated learning and differential privacy in Google Keyboard . |
| Outcome: | The proposed algorithm achieves meaningfully formal DP guarantees without uniform sampling of clients. |
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems (2024.emnlp-industry)
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| Challenge: | Differential privacy (DP) and federated learning (FL) are used for language models training in production mobile keyboard applications. |
| Approach: | They propose a variant of DP-FTRL that uses a correlated noise mechanism to train on-device language models. |
| Outcome: | The proposed method improves privacy-utility trade-off and memory efficiency over existing FL methods while simplifying usage requirements and reducing memory. |