FedQMIX:Communication-efficient federated learning via multi-agent reinforcement learning  

作　　者：Shaohua Cao Hanqing Zhang Tian Wen Hongwei Zhao Quancheng Zheng Weishan Zhang Danyang Zheng 

机构地区：[1]Qingdao Institute of Software,College of Computer Science and Technology,China University of Petroleum(East China),Qingdao 266580,China [2]School of Computing and Artificial Intelligence,Xipu Campus,Southwest Jiaotong University,Chengdu 611756,China

出　　处：《High-Confidence Computing》2024年第2期96-104,共9页高置信计算（英文）

基　　金：supported by the National Natural Science Foundation of China(NSFC)(62072469)。

摘　　要：Since the data samples on client devices are usually non-independent and non-identically distributed(non-IID),this will challenge the convergence of federated learning(FL)and reduce communication efficiency.This paper proposes FedQMIX,a node selection algorithm based on multi-agent reinforcement learning(MARL),to address these challenges.Firstly,we observe a connection between model weights and data distribution,and a clustering algorithm can group clients with similar data distribution into the same cluster.Secondly,we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward,penalizing the use of more communication rounds and thereby improving the communication efficiency of FL.Finally,experiments show that FedQMIX can reduce the number of communication rounds by 11%and 30%on the MNIST and CIFAR-10 datasets,respectively,compared to the baseline algorithm(Favor).

关 键 词：Communication efficient Federated learning MARL 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]