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ShanghaiTech University Knowledge Management System
IRS-Assisted Digital Over-the-Air Federated Learning | |
2023 | |
会议录名称 | PROCEEDINGS - IEEE GLOBAL COMMUNICATIONS CONFERENCE, GLOBECOM |
ISSN | 2334-0983 |
页码 | 3276-3281 |
发表状态 | 已发表 |
DOI | 10.1109/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 |
EISSN | 2576-6813 |
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. |
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