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research article

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Yuan, Kun  
•
Ying, Bicheng  
•
Liu, Jiageng
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January 15, 2019
Ieee Transactions On Signal Processing

This paper develops a distributed variance-reduced strategy for a collection of interacting agents that are connected by a graph topology. The resulting diffusion-AVRG (where AVRG stands for "amortized variance-reduced gradient") algorithm is shown to have linear convergence to the exact solution, and is more memory efficient than other alternative algorithms. When a batch implementation is employed, it is observed in simulations that diffusion-AVRG is more computationally efficient than exact diffusion or EXTRA, while maintaining almost the same communication efficiency.

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Type
research article
DOI
10.1109/TSP.2018.2872003
Web of Science ID

WOS:000452618000006

Author(s)
Yuan, Kun  
Ying, Bicheng  
Liu, Jiageng
Sayed, Ali H.  
Date Issued

2019-01-15

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Published in
Ieee Transactions On Signal Processing
Volume

67

Issue

2

Start page

351

End page

366

Subjects

Engineering, Electrical & Electronic

•

Engineering

•

diffusion strategy

•

variance-reduction

•

stochastic gradient descent

•

memory efficiency

•

avrg

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mini-batch

•

convergence

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algorithm

•

admm

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
ASL  
Available on Infoscience
January 23, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/153979
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