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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14929 |
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| _version_ | 1866911172797661184 |
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| author | Wang, Ganyu Fang, Jinjie Yin, Maxwell J. Gu, Bin Chen, Xi Wang, Boyu Chang, Yi Ling, Charles |
| author_facet | Wang, Ganyu Fang, Jinjie Yin, Maxwell J. Gu, Bin Chen, Xi Wang, Boyu Chang, Yi Ling, Charles |
| contents | Black-Box Discrete Prompt Learning is a prompt-tuning method that optimizes discrete prompts without accessing model parameters or gradients, making the prompt tuning on a cloud-based Large Language Model (LLM) feasible. Adapting federated learning to BDPL could further enhance prompt tuning performance by leveraging data from diverse sources. However, all previous research on federated black-box prompt tuning had neglected the substantial query cost associated with the cloud-based LLM service. To address this gap, we conducted a theoretical analysis of query efficiency within the context of federated black-box prompt tuning. Our findings revealed that degrading FedAvg to activate only one client per round, a strategy we called \textit{FedOne}, enabled optimal query efficiency in federated black-box prompt learning. Building on this insight, we proposed the FedOne framework, a federated black-box discrete prompt learning method designed to maximize query efficiency when interacting with cloud-based LLMs. We conducted numerical experiments on various aspects of our framework, demonstrating a significant improvement in query efficiency, which aligns with our theoretical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning Wang, Ganyu Fang, Jinjie Yin, Maxwell J. Gu, Bin Chen, Xi Wang, Boyu Chang, Yi Ling, Charles Machine Learning Black-Box Discrete Prompt Learning is a prompt-tuning method that optimizes discrete prompts without accessing model parameters or gradients, making the prompt tuning on a cloud-based Large Language Model (LLM) feasible. Adapting federated learning to BDPL could further enhance prompt tuning performance by leveraging data from diverse sources. However, all previous research on federated black-box prompt tuning had neglected the substantial query cost associated with the cloud-based LLM service. To address this gap, we conducted a theoretical analysis of query efficiency within the context of federated black-box prompt tuning. Our findings revealed that degrading FedAvg to activate only one client per round, a strategy we called \textit{FedOne}, enabled optimal query efficiency in federated black-box prompt learning. Building on this insight, we proposed the FedOne framework, a federated black-box discrete prompt learning method designed to maximize query efficiency when interacting with cloud-based LLMs. We conducted numerical experiments on various aspects of our framework, demonstrating a significant improvement in query efficiency, which aligns with our theoretical results. |
| title | FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.14929 |