Empowering over-the-air personalized federated learning via RIS

被引：0

作者：

Wei SHI ^{[1
,2
]}

Jiacheng YAO ^{[1
,2
]}

Jindan XU ^{[3
]}

Wei XU ^{[1
,2
]}

Lexi XU ^{[4
]}

Chunming ZHAO ^{[1
,2
]}

机构：

[1] National Mobile Communications Research Laboratory, Southeast University

[2] Purple Mountain Laboratories

[3] School of Electrical and Electronics Engineering, Nanyang Technological University

[4] Research Institute, China United Network Communications

来源：

Science China(Information Sciences) | 2024年 / 67卷 / 11期

关键词：

D O I：

暂无

中图分类号：

学科分类号：

摘要：

<正>Federated learning(FL) is a promising distributed learning approach due to its privacy-enhancing characteristic [1–3].To enhance communication efficiency of FL, over-the-air computation(AirComp) has emerged as a key technique by exploiting the waveform superposition property of multiple access channels [4, 5]. Although AirComp-enabled FL(AirFL) offers significant performance gains, it does not address the data heterogeneity in most real-life FL scenarios with non-independent and identically distributed local datasets.

引用

页码：371 / 372

页数：2

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