@inproceedings{jianhao-etal-2024-promoting,
title = "Promoting Data and Model Privacy in Federated Learning through Quantized {L}o{RA}",
author = "JianHao, Zhu and
Lv, Changze and
Wang, Xiaohua and
Wu, Muling and
Liu, Wenhao and
Li, Tianlong and
Ling, Zixuan and
Zhang, Cenyuan and
Zheng, Xiaoqing and
Huang, Xuanjing",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.615",
doi = "10.18653/v1/2024.findings-emnlp.615",
pages = "10501--10512",
abstract = "Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational resources, rendering them valuable intellectual properties for their developers and owners. To establish a mechanism that protects both data and model privacy in a federated learning context, we introduce a method that just needs to distribute a quantized version of the model{'}s parameters during training. This method enables accurate gradient estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. Moreover, we combine this quantization strategy with LoRA, a popular and parameter-efficient fine-tuning method, to significantly reduce communication costs in federated learning. The proposed framework, named FedLPP, successfully ensures both data and model privacy in the federated learning context. Additionally, the learned central model exhibits good generalization and can be trained in a resource-efficient manner.",
}
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<abstract>Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational resources, rendering them valuable intellectual properties for their developers and owners. To establish a mechanism that protects both data and model privacy in a federated learning context, we introduce a method that just needs to distribute a quantized version of the model’s parameters during training. This method enables accurate gradient estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. Moreover, we combine this quantization strategy with LoRA, a popular and parameter-efficient fine-tuning method, to significantly reduce communication costs in federated learning. The proposed framework, named FedLPP, successfully ensures both data and model privacy in the federated learning context. Additionally, the learned central model exhibits good generalization and can be trained in a resource-efficient manner.</abstract>
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%0 Conference Proceedings
%T Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
%A JianHao, Zhu
%A Lv, Changze
%A Wang, Xiaohua
%A Wu, Muling
%A Liu, Wenhao
%A Li, Tianlong
%A Ling, Zixuan
%A Zhang, Cenyuan
%A Zheng, Xiaoqing
%A Huang, Xuanjing
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F jianhao-etal-2024-promoting
%X Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational resources, rendering them valuable intellectual properties for their developers and owners. To establish a mechanism that protects both data and model privacy in a federated learning context, we introduce a method that just needs to distribute a quantized version of the model’s parameters during training. This method enables accurate gradient estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. Moreover, we combine this quantization strategy with LoRA, a popular and parameter-efficient fine-tuning method, to significantly reduce communication costs in federated learning. The proposed framework, named FedLPP, successfully ensures both data and model privacy in the federated learning context. Additionally, the learned central model exhibits good generalization and can be trained in a resource-efficient manner.
%R 10.18653/v1/2024.findings-emnlp.615
%U https://aclanthology.org/2024.findings-emnlp.615
%U https://doi.org/10.18653/v1/2024.findings-emnlp.615
%P 10501-10512
Markdown (Informal)
[Promoting Data and Model Privacy in Federated Learning through Quantized LoRA](https://aclanthology.org/2024.findings-emnlp.615) (JianHao et al., Findings 2024)
ACL
- Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, and Xuanjing Huang. 2024. Promoting Data and Model Privacy in Federated Learning through Quantized LoRA. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10501–10512, Miami, Florida, USA. Association for Computational Linguistics.