SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917732655562752 |
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| author | Cao, Linxiao Zhu, Yifei Gong, Wei |
| author_facet | Cao, Linxiao Zhu, Yifei Gong, Wei |
| contents | Large pre-trained models have exhibited remarkable achievements across various domains. The substantial training costs associated with these models have led to wide studies of fine-tuning for effectively harnessing their capabilities in solving downstream tasks. Yet, conventional fine-tuning approaches become infeasible when the model lacks access to downstream data due to privacy concerns. Naively integrating fine-tuning approaches with the emerging federated learning frameworks incurs substantial communication overhead and exerts high demand on local computing resources, making it impractical for common resource-limited devices. In this paper, we introduce SFPrompt, an innovative privacy-preserving fine-tuning method tailored for the federated setting where direct uploading of raw data is prohibited and local devices are resource-constrained to run a complete pre-trained model. In essence, SFPrompt judiciously combines split learning with federated learning to handle these challenges. Specifically, the pre-trained model is first partitioned into client and server components, thereby streamlining the client-side model and substantially alleviating computational demands on local resources. SFPrompt then introduces soft prompts into the federated model to enhance the fine-tuning performance. To further reduce communication costs, a novel dataset pruning algorithm and a local-loss update strategy are devised during the fine-tuning process. Extensive experiments demonstrate that SFPrompt delivers competitive performance as the federated full fine-tuning approach while consuming a mere 0.46% of local computing resources and incurring 53% less communication cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17533 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices Cao, Linxiao Zhu, Yifei Gong, Wei Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Large pre-trained models have exhibited remarkable achievements across various domains. The substantial training costs associated with these models have led to wide studies of fine-tuning for effectively harnessing their capabilities in solving downstream tasks. Yet, conventional fine-tuning approaches become infeasible when the model lacks access to downstream data due to privacy concerns. Naively integrating fine-tuning approaches with the emerging federated learning frameworks incurs substantial communication overhead and exerts high demand on local computing resources, making it impractical for common resource-limited devices. In this paper, we introduce SFPrompt, an innovative privacy-preserving fine-tuning method tailored for the federated setting where direct uploading of raw data is prohibited and local devices are resource-constrained to run a complete pre-trained model. In essence, SFPrompt judiciously combines split learning with federated learning to handle these challenges. Specifically, the pre-trained model is first partitioned into client and server components, thereby streamlining the client-side model and substantially alleviating computational demands on local resources. SFPrompt then introduces soft prompts into the federated model to enhance the fine-tuning performance. To further reduce communication costs, a novel dataset pruning algorithm and a local-loss update strategy are devised during the fine-tuning process. Extensive experiments demonstrate that SFPrompt delivers competitive performance as the federated full fine-tuning approach while consuming a mere 0.46% of local computing resources and incurring 53% less communication cost. |
| title | SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2407.17533 |