面向多层无线边缘环境下的联邦学习通信优化的研究

现有的联邦学习模型同步方法大多基于单层的参数服务器架构,难以适应当前异构无线网络场景,同时存在单点通信负载过重、系统延展性差等问题.针对这些问题,文中提出了一种面向边缘混合无线网络的联邦学习高效模型同步方法.在混合无线网络环境中,边缘移动终端将本地模型传输给附近的小型基站,小型基站收到边缘移动终端模型后执行聚合算法,并...

Study on Communication Optimization of Federated Learning in Multi-layer Wireless Edge Environment

Existing model synchronization mechanisms of federated learning(FL)are mostly based on single-layer parameter server architecture,which are difficult to adapt to current heterogeneous wireless network scenarios.There are some problems such as excessive communication load on single-point and poor scalability of FL.In response to these problems,this paper proposes an efficient model synchronization scheme for FL in hybrid wireless edge networks.In a hybrid edge wireless network,edge devices transmit local models to nearby small base stations.After receiving local models from edge devices,small base stations execute the aggregation algorithm and send the aggregated models to the macro base station to update the global model.Considering the heterogeneity of channel performance and the competitive relationship of data transmission on the wireless channel,this paper proposes a new type of grouping asynchronous model synchronization scheme and designs a transmission rate aware channel allocation algorithm.Experiments are carried out on real data sets.Experimental results show that the proposed transmission rate aware channel allocation algorithm in grouping asynchronous model synchronization scheme can reduce communication time by 25%~60%and greatly improve the training efficiency of FL.