VLC/RF混合系统中基于纵向联邦学习的资源优化算法
Resource Optimization Algorithm Based on Vertical Federated Learning in VLC/RF Hybrid Systems
杜忠田 1黄武威 2杨洋2
作者信息
- 1. 中电信数智科技有限公司科技创新部,北京 100035
- 2. 北京邮电大学信息与通信工程学院,北京 100876
- 折叠
摘要
为解决纵向联邦学习中的通信资源受限问题,提出一种可见光通信与射频通信混合系统下联合优化传输功率、用户选择与信道估计的纵向联邦学习算法.首先,在传统射频(RF)链路的基础上引入可见光通信(VLC)链路构建VLC/RF混合系统.然后,引入基于多层感知机的信道估计算法,提高传输数据的准确性.最后,构建以最小化纵向联邦学习损失函数为目标的优化问题,并通过协同优化传输功率与用户选择求解该问题.仿真结果表明,所提算法相比现有方法模型精度分别提高了7.2%与18.2%.
Abstract
Herein,we propose an algorithm to address the issue of communication resource limitations in vertical federated learning.The vertical federated learning algorithm is designed to simultaneously optimize transmission power,user selection,and channel estimation with a hybrid system combining visible light communication(VLC)and radio-frequency(RF)communication.The first step involves constructing a VLC/RF hybrid system by introducing a VLC link in a traditional RF link.Following this,we introduce a channel estimation algorithm based on multilayer perceptron to improve the accuracy of transmitted data.The final step involves establishing an optimization problem to minimize the longitudinal federated learning loss function.This problem is then solved by co-optimizing transmission power and user selection.The simulation results show that the accuracy of the proposed algorithm is improved by 7.2%and 18.2%,respectively,compared with the existing method.
关键词
纵向联邦学习/可见光通信/资源分配/用户选择Key words
vertical federated learning/visible light communication/resource allocation/user selection引用本文复制引用
基金项目
国家自然科学基金(62371065)
中央高校基本科研业务费专项(2021XD-A01-1)
出版年
2024
NETL
NSTL
万方数据