One-bit over-the-air aggregation for communication-efficient federated edge learning: design and convergence analysis
File(s)
Accepted version
Author(s)
Zhu, Guangxu
Du, Yuqing
Gunduz, Deniz
Huang, Kaibin
Type
Journal Article
Abstract
Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromising their privacy. In the FEEL framework, edge devices periodically transmit high-dimensional stochastic gradients to the edge server, where these gradients are aggregated and used to update a global model. When the edge devices share the same communication medium, the multiple access channel (MAC) from the devices to the edge server induces a communication bottleneck. To overcome this bottleneck, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients (or local models) via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation (OBDA). The new scheme features one-bit gradient quantization followed by digital quadrature amplitude modulation (QAM) at edge devices and over-the-air majority-voting based decoding at edge server. We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, we show that all the negative effects vanish as the number of participating devices grows, but at a different rate for each type of channel hostility.
Date Issued
2020-11-26
Date Acceptance
2020-11-11
Citation
IEEE Transactions on Wireless Communications, 2020, 20 (3), pp.2120-2135
ISSN
1536-1276
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2120
End Page
2135
Journal / Book Title
IEEE Transactions on Wireless Communications
Volume
20
Issue
3
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Identifier
https://ieeexplore.ieee.org/document/9272666
Grant Number
677854
Subjects
Networking & Telecommunications
0805 Distributed Computing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
Publication Status
Published
Date Publish Online
2020-11-26