Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909101281247232 |
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| author | Che, Tianshi Liu, Ji Zhou, Yang Ren, Jiaxiang Zhou, Jiwen Sheng, Victor S. Dai, Huaiyu Dou, Dejing |
| author_facet | Che, Tianshi Liu, Ji Zhou, Yang Ren, Jiaxiang Zhou, Jiwen Sheng, Victor S. Dai, Huaiyu Dou, Dejing |
| contents | Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15080 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization Che, Tianshi Liu, Ji Zhou, Yang Ren, Jiaxiang Zhou, Jiwen Sheng, Victor S. Dai, Huaiyu Dou, Dejing Machine Learning Computation and Language Distributed, Parallel, and Cluster Computing Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO. |
| title | Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization |
| topic | Machine Learning Computation and Language Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2310.15080 |