智能边缘光网络的3-CS分布式联邦迁移学习框架

Hui Yang, Q. Yao, B. Bao, Chao Li, Danshi Wang, Jie Zhang, M. Cheriet
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引用次数: 1

摘要

随着光网络和边缘计算的快速发展,边缘光网络的运行效率变得越来越重要,需要一种智能的方式来提升网络性能。为了提高边缘光网络的智能化,本文首先提出了边缘光网络发展的需求。然后,提出了一种用于边缘光网络的跨场景、跨频谱、跨业务(3-CS)架构。最后,提出了一个实现分布式智能边缘光网络的联邦迁移学习(FTL)框架。通过仿真验证了该框架的可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks
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.
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