@inproceedings{wang-etal-2024-public,
title = "Can Public Large Language Models Help Private Cross-device Federated Learning?",
author = "Wang, Boxin and
Zhang, Yibo and
Cao, Yuan and
Li, Bo and
McMahan, Hugh and
Oh, Sewoong and
Xu, Zheng and
Zaheer, Manzil",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.59",
doi = "10.18653/v1/2024.findings-naacl.59",
pages = "934--949",
abstract = "We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful formal user-level differential privacy (DP) guarantees when massive parallelism in training is enabled by the participation of a moderate size of users. Recently, public data has been used to improve privacy-utility trade-offs for both large and small language models. In this work, we provide a systematic study of using large-scale public data and LLMs to help differentially private training of on-device FL models, and further improve the privacy-utility tradeoff by techniques of distillation. Moreover, we propose a novel distribution matching algorithm with theoretical grounding to sample public data close to private data distribution, which significantly improves the sample efficiency of (pre-)training on public data. The proposed method is efficient and effective for training private models by taking advantage of public data, especially for customized on-device architectures that do not have ready-touse pre-trained models.",
}
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<abstract>We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful formal user-level differential privacy (DP) guarantees when massive parallelism in training is enabled by the participation of a moderate size of users. Recently, public data has been used to improve privacy-utility trade-offs for both large and small language models. In this work, we provide a systematic study of using large-scale public data and LLMs to help differentially private training of on-device FL models, and further improve the privacy-utility tradeoff by techniques of distillation. Moreover, we propose a novel distribution matching algorithm with theoretical grounding to sample public data close to private data distribution, which significantly improves the sample efficiency of (pre-)training on public data. The proposed method is efficient and effective for training private models by taking advantage of public data, especially for customized on-device architectures that do not have ready-touse pre-trained models.</abstract>
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%0 Conference Proceedings
%T Can Public Large Language Models Help Private Cross-device Federated Learning?
%A Wang, Boxin
%A Zhang, Yibo
%A Cao, Yuan
%A Li, Bo
%A McMahan, Hugh
%A Oh, Sewoong
%A Xu, Zheng
%A Zaheer, Manzil
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wang-etal-2024-public
%X We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful formal user-level differential privacy (DP) guarantees when massive parallelism in training is enabled by the participation of a moderate size of users. Recently, public data has been used to improve privacy-utility trade-offs for both large and small language models. In this work, we provide a systematic study of using large-scale public data and LLMs to help differentially private training of on-device FL models, and further improve the privacy-utility tradeoff by techniques of distillation. Moreover, we propose a novel distribution matching algorithm with theoretical grounding to sample public data close to private data distribution, which significantly improves the sample efficiency of (pre-)training on public data. The proposed method is efficient and effective for training private models by taking advantage of public data, especially for customized on-device architectures that do not have ready-touse pre-trained models.
%R 10.18653/v1/2024.findings-naacl.59
%U https://aclanthology.org/2024.findings-naacl.59
%U https://doi.org/10.18653/v1/2024.findings-naacl.59
%P 934-949
Markdown (Informal)
[Can Public Large Language Models Help Private Cross-device Federated Learning?](https://aclanthology.org/2024.findings-naacl.59) (Wang et al., Findings 2024)
ACL
- Boxin Wang, Yibo Zhang, Yuan Cao, Bo Li, Hugh McMahan, Sewoong Oh, Zheng Xu, and Manzil Zaheer. 2024. Can Public Large Language Models Help Private Cross-device Federated Learning?. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 934–949, Mexico City, Mexico. Association for Computational Linguistics.