Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling
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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Yuan, K., Ying, B., Liu, J., & Sayed, A. H. (2019). Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling. Ieee Transactions on Signal Processing, 67(2), 351–366. https://doi.org/10.1109/TSP.2018.2872003
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WOS:000452618000006
2019-01-15
67
2
351
366
REVIEWED
EPFL