消息
×
loading..
IRS-Assisted Digital Over-the-Air Federated Learning
2023
会议录名称PROCEEDINGS - IEEE GLOBAL COMMUNICATIONS CONFERENCE, GLOBECOM
ISSN2334-0983
页码3276-3281
发表状态已发表
DOI10.1109/GLOBECOM54140.2023.10436811
摘要

For the purpose of training a machine learning model via exploiting data from multiple devices without compromising their privacy, federated learning (FL) has become a popular approach. Meanwhile, over-the-air computation (AirComp) enables concurrent model transmission to accelerate model aggregation in the context of FL. However, the performance of model aggregation is significantly hindered by adverse wireless channels. In this paper, we employ intelligent reflecting surface (IRS) to facilitate accurate model aggregation in AirComp-based FL. To ensure compatibility with existing communication standards, this paper adopts uniform quantization for both downlink model broadcast and uplink AirComp-based gradient aggregation. Furthermore, we quantitatively examine the impact of quantization errors on transmission accuracy and convergence bound. To mitigate signal distortion, we employ an alternating optimization algorithm that optimizes the beamforming vector at the base station, the transmit/receive scalars at the devices, and the phase shifts at the IRS. The simulation results provide compelling evidence for the effectiveness and robustness of our proposed method. © 2023 IEEE.

关键词Quantization (signal) Atmospheric modeling Computational modeling Wireless networks Downlink Uplink Context modeling
会议名称2023 IEEE Global Communications Conference, GLOBECOM 2023
会议地点Kuala Lumpur, Malaysia
会议日期December 4, 2023 - December 8, 2023
URL查看原文
收录类别EI
语种英语
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20241115723740
EISSN2576-6813
原始文献类型Conference article (CA)
来源库IEEE
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/359966
专题信息科学与技术学院
信息科学与技术学院_PI研究组_周勇组
信息科学与技术学院_硕士生
信息科学与技术学院_博士生
作者单位
School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China
第一作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
推荐引用方式
GB/T 7714
Pan, Yudi,Wang, Zhibin,Wu, Liantao,et al. IRS-Assisted Digital Over-the-Air Federated Learning[C]:Institute of Electrical and Electronics Engineers Inc.,2023:3276-3281.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Pan, Yudi]的文章
[Wang, Zhibin]的文章
[Wu, Liantao]的文章
百度学术
百度学术中相似的文章
[Pan, Yudi]的文章
[Wang, Zhibin]的文章
[Wu, Liantao]的文章
必应学术
必应学术中相似的文章
[Pan, Yudi]的文章
[Wang, Zhibin]的文章
[Wu, Liantao]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。