Federated learning secrecy rate optimization for 6G intelligent reflecting surface assistance

WU Yingying; MAO Bomin

doi:10.11959/j.issn.2096-109x.2025021

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Federated learning secrecy rate optimization for 6G intelligent reflecting surface assistance

Papers | 更新时间：2025-05-14

- Federated learning secrecy rate optimization for 6G intelligent reflecting surface assistance
- Chinese Journal of Network and Information Security Vol. 11, Issue 2, Pages: 115-124(2025)
- 作者机构：
  
  1.西北工业大学网络空间安全学院，陕西西安 710129
  2.西北工业大学太仓长三角研究院，江苏太仓 215400
- 作者简介：
- 基金信息：
  
  The National Key R&D Program of China(2024YFF1401305)
- DOI：10.11959/j.issn.2096-109x.2025021
  CLC： TN915.08
- Received：29 October 2024，
  
  Revised：2025-03-02，
  
  Published：25 April 2025
- 稿件说明：
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武影影, 毛伯敏. 面向6G智能反射面辅助的联邦学习安全速率优化[J]. 网络与信息安全学报, 2025, 11(2): 115-124.

WU Y Y, MAO B M. Federated learning secrecy rate optimization for 6G intelligent reflecting surface assistance[J]. Chinese Journal of Network and Information Security, 2025, 11(2): 115-124.
武影影, 毛伯敏. 面向6G智能反射面辅助的联邦学习安全速率优化[J]. 网络与信息安全学报, 2025, 11(2): 115-124. DOI： 10.11959/j.issn.2096-109x.2025021.

WU Y Y, MAO B M. Federated learning secrecy rate optimization for 6G intelligent reflecting surface assistance[J]. Chinese Journal of Network and Information Security, 2025, 11(2): 115-124. DOI： 10.11959/j.issn.2096-109x.2025021.

摘要

基于非正交多址接入的联邦学习可以实现6G物联网设备的大规模接入、高速率传输及泛在智能。然而，无线信道的随机性导致模型参数传输时延波动，而频繁的模型更新可能引发通信-计算资源竞争；同时，本地模型上传阶段可能遭遇窃听攻击，导致用户隐私泄露。为了解决上述问题，提出了智能反射面辅助的基于非正交多址接入的联邦学习通信系统，旨在利用智能反射面调控无线传输环境，增加合法接收者的信号强度并抑制窃听者的接收信号，为联邦学习构建高可靠、高安全的通信基础。首先建立智能反射面辅助的通信模型；接着通过联合优化物联网设备发送功率及智能反射面各元件相移，最大化用户-基站最小安全速率；最后利用深度确定性策略梯度对上述优化问题求解，定义发送功率和智能反射面元件相移矩阵为动作空间、信道信息和上一阶段动作为状态空间。仿真结果表明，与人工噪声方法、块坐标上升方法、不部署智能反射面及不优化智能反射面元件相移相比，部署并优化智能反射面相移可以大幅提高联邦训练模型更新过程的安全速率，为后续联邦学习算法在该通信系统中的部署提供物理层保障。

Abstract

Non-orthogonal multiple access (NOMA) based federated learning (FL) can achieve the massive connectivity of internet of thing (IoT) devices

high rate transmission

and pervasive intelligence in 6G networks. However

the stochastic channels in NOMA can lead to fluctuations in the transmission delay of model parameters. Frequent model updates may cause competition for communication and computational resources

and the uploaded model can be intercepted

leading to privacy leakage. To address these issues

an intelligent reflecting surface (IRS)-assisted NOMA-based FL communication system was proposed. In this system

IRS was leveraged to reconfigure the wireless communication environment

enhancing the signal strength at the legitimate receivers and suppressing the received signal at eavesdroppers (Eves)

thereby providing a high-reliability and high-security communication foundation for federated learning. Firstly

the IRS-assisted wireless communication model was constructed. Subsequently

the minimum user-BS secrecy rate was maximized through the joint optimization of IoT device transmit power and IRS phase shifts. Finally

the deep deterministic policy gradient (DDPG) algorithm was employed to solve the optimization problem

with the action space defined as the transmit power and IRS phase shift matrix

and the channel state information and previous action constituting the state space. Simulation results demonstrated that deploying IRS in the system can greatly enhance the secrecy rate of the FL model update compared to artificial noise (AN)

the block coordinate ascent method (BCAM)

the scheme without IRS

and the scheme with random phase IRS

providing physical layer support for the subsequent deployment of FL algorithms in this communication system.

关键词

Keywords

references

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