Saddle Point Approximation Based Delay Analysis for Wireless Federated Learning
2021
会议录名称IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
ISSN1550-3607
发表状态已发表
DOI10.1109/ICC42927.2021.9500298
摘要

Wireless federated learning (FL) holds the potential of preserving data privacy and reducing network traffic congestion, thereby attracting much recent attention. Due to the fading nature of wireless channels, wireless FL suffers from the random delay in each uplink and downlink transmission. As a result, how to analyze the overall random delay of a FL task over wireless fading channels remains open. To solve this challenging problem, we present a saddle point approximation based approach to obtain the distribution of the delay caused by communication in wireless FL systems. In particular, we obtain the uplink delay distribution and the downlink delay distribution by Lugannani-Rice formula. The overall delay distribution is then obtained through the convolution of those two distributions and the generating function. Simulation results demonstrate that the theoretical results provide accurate characterizations for the empirical results, which corroborates the validity of the analysis in this paper.

会议名称IEEE International Conference on Communications (ICC)
出版地345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点null,null,ELECTR NETWORK
会议日期JUN 14-23, 2021
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收录类别CPCI-S ; CPCI
语种英语
资助项目National Natural Science Foundation of China[61671269] ; National Key Research and Development Program[2018YFA0701601]
WOS研究方向Telecommunications
WOS类目Telecommunications
WOS记录号WOS:000719386000054
出版者IEEE
EI入藏号/
来源库IEEE
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文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/138725
专题信息科学与技术学院_PI研究组_石远明组
通讯作者Yang, Longwei
作者单位
1.Tsinghua Univ, Dept Elect Engn, BNRist, Beijing 100084, Peoples R China
2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
3.Lenovo Res, Beijing 100094, Peoples R China
推荐引用方式
GB/T 7714
Yang, Longwei,Li, Lintao,Guo, Xin,et al. Saddle Point Approximation Based Delay Analysis for Wireless Federated Learning[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021.
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