A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

被引:2
|
作者
Yang, Hui [1 ]
Yao, Qiuyan [1 ]
Bao, Bowen [1 ]
Li, Chao [1 ]
Wang, Danshi [1 ]
Zhang, Jie [1 ]
Cheriet, Mohamed [2 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing, Peoples R China
[2] Univ Quebec, Dept Syst Engn, ETS, Montreal, PQ, Canada
基金
北京市自然科学基金;
关键词
edge optical network; distributed framework; artificial intelligence; federated transfer learning; cross-scene; cross-spectrum; cross-service; OPTIMIZATION; CORE;
D O I
10.3389/frcmn.2021.700912
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
With the rapid development of optical network and edge computing, the operation efficiency of the edge optical network has become more and more important, requiring an intelligent approach to enhance the network performance. To enhance the intelligence of the edge optical network, this article firstly provides the demand for the development of edge optical networks. Then, a cross-scene, cross-spectrum, and cross-service (3-CS) architecture for edge optical networks is presented. Finally, a federated transfer learning (FTL) framework, realizing a distributed intelligence edge optical network, is proposed. The usability of the proposed framework is verified by simulation.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] A Distributed Federated Transfer Learning Framework for Edge Optical Network
    Yang, Hui
    Yao, Qiuyan
    Zhang, Jie
    2020 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE (ACP) AND INTERNATIONAL CONFERENCE ON INFORMATION PHOTONICS AND OPTICAL COMMUNICATIONS (IPOC), 2020,
  • [2] Robust federated learning for edge-intelligent networks
    Zhihe GAO
    Xiaoming CHEN
    Xiaodan SHAO
    Science China(Information Sciences), 2022, 65 (03) : 246 - 254
  • [3] Robust federated learning for edge-intelligent networks
    Gao, Zhihe
    Chen, Xiaoming
    Shao, Xiaodan
    SCIENCE CHINA-INFORMATION SCIENCES, 2022, 65 (03)
  • [4] Robust federated learning for edge-intelligent networks
    Zhihe Gao
    Xiaoming Chen
    Xiaodan Shao
    Science China Information Sciences, 2022, 65
  • [5] Federated learning framework for mobile edge computing networks
    Fantacci, Romano
    Picano, Benedetta
    CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2020, 5 (01) : 15 - 21
  • [6] Robust Design of Federated Learning for Edge-Intelligent Networks
    Qi, Qiao
    Chen, Xiaoming
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (07) : 4469 - 4481
  • [7] A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning
    Hudson, Nathaniel
    Hossain, Md Jakir
    Hosseinzadeh, Minoo
    Khamfroush, Hana
    Rahnamay-Naeini, Mahshid
    Ghani, Nasir
    30TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2021), 2021,
  • [8] A framework for energy and carbon footprint analysis of distributed and federated edge learning
    Savazzi, Stefano
    Kianoush, Sanaz
    Rampa, Vittorio
    Bennis, Mehdi
    2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2021,
  • [9] A mutual information based federated learning framework for edge computing networks
    Chen, Naiyue
    Li, Yinglong
    Liu, Xuejun
    Zhang, Zhenjiang
    COMPUTER COMMUNICATIONS, 2021, 176 (176) : 23 - 30
  • [10] An Optimization Framework for Federated Edge Learning
    Li, Yangchen
    Cui, Ying
    Lau, Vincent
    2022 IEEE 23RD INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATION (SPAWC), 2022,