FedFog- A federated learning based resource management framework in fog computing for zero touch networks

被引:1
作者
Khan, Urooj Yousuf [1 ]
Soomro, Tariq Rahim [1 ]
Kougen, Zheng [2 ]
机构
[1] Inst Business Management Karachi, Coll Comp Sci & Informat Syst, Karachi, Pakistan
[2] Zhejiang Univ, Hangzhou, Peoples R China
关键词
Federated Learning; Resource Management; Fog Computing; Internet of Things; CHALLENGES; INTERNET;
D O I
10.22581/muet1982.2303.08
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fog computing offers an optimal answer to the expansion challenge of today's networks. It boasts scaling and reduced latency. Since the concept is still nascent, many research questions remain unanswered. One of these is the challenge of Resource Management. There is a pressing need for a reliable and scalable architecture that meets the Resource Management challenge without compromising the Quality of Service. Among the proposed solutions, Artificial Intelligence based path selection techniques and automated link detection methods can provide lasting and reliable answer. An optimal approach for introducing intelligence in the networks is the infusion of Machine learning methods. Such futuristic, intelligent networks form the backbone of the next generation of Internet. These self-learning and self-healing networks are termed as the ZeroTouch networks. This paper proposes FedFog, a Federated Learning based optimal, automated Resource Management framework in Fog Computing for Zero-touch Networks. The paper describes a series of experiments focusing on Quality of Service parameters such as Network latency, Resources processed, Energy consumption and Network usage. The simulation results from these experiments depict superiority of the proposed architecture over traditional, existing architecture.
引用
收藏
页码:67 / 78
页数:12
相关论文
共 50 条
  • [21] A mutual information based federated learning framework for edge computing networks
    Chen, Naiyue
    Li, Yinglong
    Liu, Xuejun
    Zhang, Zhenjiang
    [J]. COMPUTER COMMUNICATIONS, 2021, 176 (176) : 23 - 30
  • [22] Federated Learning Collaborative Content Caching Scheme in Fog Computing Networks
    Huang X.
    Wang F.
    Chen Z.
    Chen Q.
    [J]. Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, 2023, 46 (02): : 22 - 28
  • [23] Blockchain-Enabled Clustered Federated Learning in Fog Computing Networks
    Huang, Xiaoge
    Zhi, Chen
    Chen, Qianbin
    Zhang, Jie
    [J]. 2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
  • [24] Dynamic Resource Management and Task Offloading Framework for Fog Computing
    Haitham M. Abdelghany
    [J]. Journal of Grid Computing, 2025, 23 (2)
  • [25] Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework
    Alwakeel, Ahmed M.
    Alnaim, Abdulrahman K.
    [J]. SENSORS, 2024, 24 (13)
  • [26] SM@RMFFOG: sensor mining at resource management framework of fog computing
    Sepide Masoudi
    Faramarz Safi-Esfahani
    [J]. The Journal of Supercomputing, 2022, 78 : 19188 - 19227
  • [27] SM@RMFFOG: sensor mining at resource management framework of fog computing
    Masoudi, Sepide
    Safi-Esfahani, Faramarz
    [J]. JOURNAL OF SUPERCOMPUTING, 2022, 78 (17) : 19188 - 19227
  • [28] Fog computing-based federated intrusion detection algorithm for wireless sensor networks
    Zhu M.
    Chen Z.
    Liu P.
    Lyu N.
    [J]. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (10): : 1943 - 1950
  • [29] A learning-based resource provisioning approach in the fog computing environment
    Etemadi, Masoumeh
    Ghobaei-Arani, Mostafa
    Shahidinejad, Ali
    [J]. JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2021, 33 (06) : 1033 - 1056
  • [30] Coupling resource management based on fog computing in smart city systems
    Wang, Tian
    Liang, Yuzhu
    Jia, Weijia
    Arif, Muhammad
    Liu, Anfeng
    Xie, Mande
    [J]. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2019, 135 : 11 - 19