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research article
Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling
January 15, 2019
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.
Type
research article
Web of Science ID
WOS:000452618000006
Author(s)
Date Issued
2019-01-15
Published in
Volume
67
Issue
2
Start page
351
End page
366
Peer reviewed
REVIEWED
Written at
EPFL
EPFL units
Available on Infoscience
January 23, 2019
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