Adaptive Resource Optimization for Federated Learning in UAV Digital Twin Edge Networks

Xie Zheng-hao; Lai Jian-xin; Zhuang Xiao-chong; Jiang Li

doi:10.12052/gdutxb.240005

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Adaptive Resource Optimization for Federated Learning in UAV Digital Twin Edge Networks

更新时间：2026-05-27

- Adaptive Resource Optimization for Federated Learning in UAV Digital Twin Edge Networks
- Vol. 41, Issue 4, Pages: 61-69(2024)
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 广东工业大学物联网信息技术广东省重点实验室,广东,广州,510006
  3. 智能检测与制造物联教育部重点实验室,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240005
  CLC： TN929.5
- Published：2024
- 稿件说明：
移动端阅览
谢正昊, 赖健鑫, 庄晓翀, 蒋丽. 面向无人机数字孪生边缘网络的联邦学习资源自适应优化机制[J]. 广东工业大学学报, 2024, 41(4): 61-69.

Xie Zheng-hao, Lai Jian-xin, Zhuang Xiao-chong, et al. Adaptive Resource Optimization for Federated Learning in UAV Digital Twin Edge Networks[J]. 2024, 41(4): 61-69.
谢正昊, 赖健鑫, 庄晓翀, 蒋丽. 面向无人机数字孪生边缘网络的联邦学习资源自适应优化机制[J]. 广东工业大学学报, 2024, 41(4): 61-69. DOI： 10.12052/gdutxb.240005.

Xie Zheng-hao, Lai Jian-xin, Zhuang Xiao-chong, et al. Adaptive Resource Optimization for Federated Learning in UAV Digital Twin Edge Networks[J]. 2024, 41(4): 61-69. DOI： 10.12052/gdutxb.240005.

摘要

为了解决无人机数字孪生边缘网络联邦学习性能优化问题，本文提出一种基于深度强化学习的无人机数字孪生边缘网络资源调度策略。考虑动态时变的无人机数字孪生边缘网络环境，构建包含地面基站(Base Station

BS)、地面智能终端、空中无人机以及无线传输信道的孪生网络模型，建立联合无人机飞行距离、飞行角度以及无线网络频谱资源分配的自适应资源优化模型，实现最小化联邦学习时延的目标。在无人机数字孪生边缘网络环境下，提出多智能体深度确定性策略梯度算法(Multi-Agent Deep Deterministic Policy Gradient，MA-DDPG)，求解自适应资源优化模型。算法训练过程采用中心化训练、去中心化执行的方式，每个无人机智能体在评估动作价值时会考虑其他智能体的状态和动作，而在执行时只根据自身的局部观察来决定动作。上述训练过程将在数字孪生环境中执行，算法收敛后再应用于真实世界，最大限度地减少物理实体的资源开销。仿真结果表明，所提算法可显著降低联邦学习服务时延，同时保证联邦学习训练损失和准确率的优越性。

Abstract

To address the performance optimization issues in federated learning for unmanned aerial vehicle (UAV) digital twin edge networks

a resource scheduling strategy is proposed based on deep reinforcement learning for UAV digital twin edge networks. Considering dynamic and time varying features of UAV digital twin edge networks environment

a twin network model is built consisting of base station (BS)

intelligent terminals

UAV and wireless transmission channel. Then an adaptive resource optimization model is formulated which jointly optimized UAV flying distance

flying angle and spectrum resource allocation

in order to minimize time delay of federated learning. Moreover

an UAV digital twin edge networks empowered multi-agent deep deterministic policy gradient (MA-DDPG) algorithm is designed to solve the adaptive resource optimization model. The algorithm training process adopts centralized training and decentralized execution. Each UAV agent considers the states and actions of other agents when evaluating the value of actions

but decides actions based only on its own local observations during execution. The above training process is conducted in digital twin environment

and after the algorithm converges

and it is applied to the real world

minimizing the resource overhead of physical entities to the greatest extent. Numerical results illustrate that the proposed algorithm can significantly decrease the service latency of federated learning

while guaranteeing the superiority of training loss and accuracy of federated learning.

关键词

Keywords

references

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