Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions
Edge computing has emerged as a vital paradigm for processing data near its source, significantly reducing latency and improving data privacy. Simultaneously, large language models (LLMs) such as GPT-4 and BERT have showcased impressive capabilities in data analysis, natural language processing, and...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Computer Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1538277/full |
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| author | Xuanzheng Wang Zhipeng Xu Xingfei Sui |
| author_facet | Xuanzheng Wang Zhipeng Xu Xingfei Sui |
| author_sort | Xuanzheng Wang |
| collection | DOAJ |
| description | Edge computing has emerged as a vital paradigm for processing data near its source, significantly reducing latency and improving data privacy. Simultaneously, large language models (LLMs) such as GPT-4 and BERT have showcased impressive capabilities in data analysis, natural language processing, and decision-making. This survey explores the intersection of these two domains, specifically focusing on the adaptation and optimization of LLMs for data analysis tasks in edge computing environments. We examine the challenges faced by resource-constrained edge devices, including limited computational power, energy efficiency, and network reliability. Additionally, we discuss how recent advancements in model compression, distributed learning, and edge-friendly architectures are addressing these challenges. Through a comprehensive review of the current research, we analyze the applications, challenges, and future directions of deploying LLMs in edge computing. This analysis aims to facilitate intelligent data analysis across various industries, including healthcare, smart cities, and the internet of things. |
| format | Article |
| id | doaj-art-558eaa4b82ca4a21abc6374d60388347 |
| institution | OA Journals |
| issn | 2624-9898 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj-art-558eaa4b82ca4a21abc6374d603883472025-08-20T01:49:12ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982025-05-01710.3389/fcomp.2025.15382771538277Intelligent data analysis in edge computing with large language models: applications, challenges, and future directionsXuanzheng WangZhipeng XuXingfei SuiEdge computing has emerged as a vital paradigm for processing data near its source, significantly reducing latency and improving data privacy. Simultaneously, large language models (LLMs) such as GPT-4 and BERT have showcased impressive capabilities in data analysis, natural language processing, and decision-making. This survey explores the intersection of these two domains, specifically focusing on the adaptation and optimization of LLMs for data analysis tasks in edge computing environments. We examine the challenges faced by resource-constrained edge devices, including limited computational power, energy efficiency, and network reliability. Additionally, we discuss how recent advancements in model compression, distributed learning, and edge-friendly architectures are addressing these challenges. Through a comprehensive review of the current research, we analyze the applications, challenges, and future directions of deploying LLMs in edge computing. This analysis aims to facilitate intelligent data analysis across various industries, including healthcare, smart cities, and the internet of things.https://www.frontiersin.org/articles/10.3389/fcomp.2025.1538277/fulledge computinglarge language modelsintelligent data analysisresource-constrained devicesedge-friendly architectures |
| spellingShingle | Xuanzheng Wang Zhipeng Xu Xingfei Sui Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions Frontiers in Computer Science edge computing large language models intelligent data analysis resource-constrained devices edge-friendly architectures |
| title | Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions |
| title_full | Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions |
| title_fullStr | Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions |
| title_full_unstemmed | Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions |
| title_short | Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions |
| title_sort | intelligent data analysis in edge computing with large language models applications challenges and future directions |
| topic | edge computing large language models intelligent data analysis resource-constrained devices edge-friendly architectures |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1538277/full |
| work_keys_str_mv | AT xuanzhengwang intelligentdataanalysisinedgecomputingwithlargelanguagemodelsapplicationschallengesandfuturedirections AT zhipengxu intelligentdataanalysisinedgecomputingwithlargelanguagemodelsapplicationschallengesandfuturedirections AT xingfeisui intelligentdataanalysisinedgecomputingwithlargelanguagemodelsapplicationschallengesandfuturedirections |