Papers by Peter Kairouz
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. |
User Inference Attacks on Large Language Models (2024.emnlp-main)
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| Challenge: | a large amount of data written by humans is used to train and fine-tune large language models. |
| Approach: | They propose to infer if a user's data was used to train an LLM by using example-level differential privacy. |
| Outcome: | The proposed attacks are easy to employ and only require black-box access to an LLM and a few samples from the user. |