火力与指挥控制2024，Vol.49Issue(9)：185-190,6.DOI:10.3969/j.issn.1002-0640.2024.09.027
基于深度强化学习的边缘计算资源分配方法
Edge Computing Resource Allocation Method Based on Deep Reinforcement Learning
摘要
Abstract
The characteristics of edge computing make it have broad military application prospects.FL(Federated Learning)is introduced into edge computing.Considering the limited resources of IoT devices,FL accuracy and device energy consumption need to be taken into account.A framework combin-ing deep reinforcement learning,federated learning,and self attention mechanism(DRL-FLSL)is pro-posed to select devices and allocate resources to them,with the goal of balancing FL accuracy and device energy consumption.This framework introduces LSTM(Long Short Term Memory)to predict network state and adds a multi head self attention mechanism for more accurate information extraction.The simulation experimental results show that DRL-FLSL has super training effects and can effectively balance FL accu-racy and equipment energy consumption.关键词
深度强化学习/边缘计算/联邦学习/资源分配Key words
deep reinforcement learning/edge computing/federated learning/resource allocation分类
信息技术与安全科学引用本文复制引用
谢陶,黄迎春..基于深度强化学习的边缘计算资源分配方法[J].火力与指挥控制,2024,49(9):185-190,6.基金项目
国家自然科学基金资助项目(61971291) （61971291）
