典型文献
MSRA-Fed:A Communication-Efficient Federated Learning Method Based on Model Split and Representation Aggregate
文献摘要：
Recent years have witnessed a spurt of progress in federated learning, which can coordinate multi-participation model training while protecting the data privacy of participants. However, low communication efficiency is a bottleneck when deploying federated learning to edge computing and IoT devices due to the need to transmit a huge number of parameters during co-training. In this paper, we verify that the outputs of the last hidden layer can record the characteristics of training data. Accordingly, we propose a communication-efficient strategy based on model split and representation aggregate. Specifically, we make the client upload the outputs of the last hidden layer instead of all model parameters when participating in the aggregation, and the server distributes gradients according to the global information to revise local models. Empirical evidence from experiments verifies that our method can complete training by uploading less than one-tenth of model param-eters, while preserving the usability of the model.
文献关键词：
中图分类号：
作者姓名：
LIU Qinbo;JIN Zhihao;WANG Jiabo;LIU Yang;LUO Wenjian
作者机构：
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China;Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen 518055, China;Peng Cheng Laboratory, Shenzhen 518055, China
文献出处：
引用格式：
[1]LIU Qinbo;JIN Zhihao;WANG Jiabo;LIU Yang;LUO Wenjian-.MSRA-Fed:A Communication-Efficient Federated Learning Method Based on Model Split and Representation Aggregate)[J].中兴通讯技术（英文版）,2022(03):35-42
A类：
spurt
B类：
MSRA,Communication,Efficient,Federated,Learning,Method,Based,Model,Split,Representation,Aggregate,Recent,years,have,witnessed,progress,federated,learning,which,can,coordinate,multi,participation,training,while,protecting,data,privacy,participants,However,low,communication,efficiency,bottleneck,when,deploying,edge,computing,IoT,devices,due,need,transmit,huge,number,parameters,during,In,this,paper,verify,that,outputs,last,hidden,layer,record,characteristics,Accordingly,propose,efficient,strategy,split,representation,aggregate,Specifically,make,client,instead,participating,aggregation,server,distributes,gradients,according,global,information,revise,local,models,Empirical,evidence,from,experiments,verifies,our,method,complete,by,uploading,less,than,one,tenth,preserving,usability
AB值：
0.675496
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