Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Shao:2022:10.1109/twc.2021.3125798,
author = {Shao, Y and Gunduz, D and Liew, SC},
doi = {10.1109/twc.2021.3125798},
journal = {IEEE Transactions on Wireless Communications},
pages = {1--1},
title = {Federated edge learning with misaligned over-the-air computation},
url = {http://dx.doi.org/10.1109/twc.2021.3125798},
volume = {21},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.
AU  - Shao,Y
AU  - Gunduz,D
AU  - Liew,SC
DO  - 10.1109/twc.2021.3125798
EP  - 1
PY  - 2022///
SN  - 1536-1276
SP  - 1
TI  - Federated edge learning with misaligned over-the-air computation
T2  - IEEE Transactions on Wireless Communications
UR  - http://dx.doi.org/10.1109/twc.2021.3125798
UR  - https://ieeexplore.ieee.org/document/9614039
UR  - http://hdl.handle.net/10044/1/97829
VL  - 21
ER  -
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