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A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence

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Edge Computing and IoT: Systems, Management and Security (ICECI 2022)

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Abstract

With the rise of data storage computing and IoT technology. The increase in data volume and user demand, the accurate delivery of data and low latency during transmission become important factors that affect the end-user experience. To address this issue, previous authors have proposed the concept of edge computings. In the general environment of edge computing, reasonable scheduling of edge caches can largely achieve low latency and high efficiency, thus improving user experience. In this paper, based on existing research, we propose a combination of a joint learning framework for cache prediction based on region popularity and an edge collaborative cache value optimization method to further improve cache hit rate and cache utilization efficiency. The method obtains excellent expected results through simulation experiments.

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Acknowledgment

This work was supported in part by the Scientific research projects funded by the Department of education of Hunan Province (No. 22C0497), the Huaihua University Double First-Class initiative Applied Characteristic Discipline of Control Science and Engineering(No. ZNKZN2021-10), the National Natural Science Foundation of China (No. 62172182), the Hunan Provincial Natural Science Foundation of China (No. 2020JJ4490), the Project of Hunan Provincial Social Science Foundation (No. 21JD046), the Huaihua University Project (No. HHUY2019-25), the Philosophy and Social Science Achievement Evaluation Committee of Huaihua (No. HSP2022YB40) and the Science and Technology Innovation 2030 Special Project Sub-Topics (No. 2018AAA0102100).

Hunan University Students’ Innovation and Entrepreneurship Training Program (202210548064).

Author information

Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Huaihua University, Huaihua, 418000, Hunan, People’s Republic of China

    Zhirong Zhu, Yiwen Liu, Yanxia Gao, Wenkan Wen, Yuanquan Shi & **aoning Peng

  2. Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities, Huaihua, 418000, Hunan, People’s Republic of China

    Yiwen Liu, Yanxia Gao, Yuanquan Shi & **aoning Peng

  3. Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua, 418000, Hunan, People’s Republic of China

    Yiwen Liu, Yanxia Gao, Yuanquan Shi & **aoning Peng

Authors
  1. Zhirong Zhu
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  2. Yiwen Liu
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  3. Yanxia Gao
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  4. Wenkan Wen
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  6. **aoning Peng
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Correspondence to Yiwen Liu .

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Editors and Affiliations

  1. Hunan University, Changsha, China

    Zhu **ao

  2. Donghua University, Shanghai, China

    ** Zhao

  3. Hunan University, Changsha, China

    **ngxia Dai

  4. Hunan University, Changsha, China

    **mei Shu

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Zhu, Z., Liu, Y., Gao, Y., Wen, W., Shi, Y., Peng, X. (2023). A Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence. In: **ao, Z., Zhao, P., Dai, X., Shu, J. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 478. Springer, Cham. https://doi.org/10.1007/978-3-031-28990-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-28990-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28989-7

  • Online ISBN: 978-3-031-28990-3

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