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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.20299 |
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| _version_ | 1866929715055427584 |
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| author | Li, Jiaxing Xu, Chi Jia, Lianchen Wang, Feng Zhang, Cong Liu, Jiangchuan |
| author_facet | Li, Jiaxing Xu, Chi Jia, Lianchen Wang, Feng Zhang, Cong Liu, Jiangchuan |
| contents | Large language models (LLMs) have demonstrated impressive capabilities in language tasks, but they require high computing power and rely on static knowledge. To overcome these limitations, Retrieval-Augmented Generation (RAG) incorporates up-to-date external information into LLMs without extensive fine-tuning. Meanwhile, small language models (SLMs) deployed on edge devices offer efficiency and low latency but often struggle with complex reasoning tasks. Unfortunately, current RAG approaches are predominantly based on centralized databases and have not been adapted to address the distinct constraints associated with deploying SLMs in edge environments. To bridge this gap, we propose Edge-Assisted and Collaborative RAG (EACO-RAG), a lightweight framework that leverages distributed edge nodes for adaptive knowledge updates and retrieval. EACO-RAG also employs a hierarchical collaborative gating mechanism to dynamically select among local, edge-assisted, and cloud-based strategies, with a carefully designed algorithm based on Safe Online Bayesian Optimization to maximize the potential performance enhancements. Experimental results demonstrate that EACO-RAG matches the accuracy of cloud-based knowledge graph RAG systems while reducing total costs by up to 84.6% under relaxed delay constraints and by 65.3% under stricter delay requirements. This work represents our initial effort toward achieving a distributed and scalable tiered LLM deployments, with EACO-RAG serving as a promising first step in unlocking the full potential of hybrid edge-cloud intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update Li, Jiaxing Xu, Chi Jia, Lianchen Wang, Feng Zhang, Cong Liu, Jiangchuan Distributed, Parallel, and Cluster Computing Large language models (LLMs) have demonstrated impressive capabilities in language tasks, but they require high computing power and rely on static knowledge. To overcome these limitations, Retrieval-Augmented Generation (RAG) incorporates up-to-date external information into LLMs without extensive fine-tuning. Meanwhile, small language models (SLMs) deployed on edge devices offer efficiency and low latency but often struggle with complex reasoning tasks. Unfortunately, current RAG approaches are predominantly based on centralized databases and have not been adapted to address the distinct constraints associated with deploying SLMs in edge environments. To bridge this gap, we propose Edge-Assisted and Collaborative RAG (EACO-RAG), a lightweight framework that leverages distributed edge nodes for adaptive knowledge updates and retrieval. EACO-RAG also employs a hierarchical collaborative gating mechanism to dynamically select among local, edge-assisted, and cloud-based strategies, with a carefully designed algorithm based on Safe Online Bayesian Optimization to maximize the potential performance enhancements. Experimental results demonstrate that EACO-RAG matches the accuracy of cloud-based knowledge graph RAG systems while reducing total costs by up to 84.6% under relaxed delay constraints and by 65.3% under stricter delay requirements. This work represents our initial effort toward achieving a distributed and scalable tiered LLM deployments, with EACO-RAG serving as a promising first step in unlocking the full potential of hybrid edge-cloud intelligence. |
| title | EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.20299 |