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arXiv · 2101.09583

Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs

Yiyue Chen·Abolfazl Hashemi·Haris Vikalo
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Abstract

We consider the problem of decentralized optimization over time-varying directed networks. The network nodes can access only their local objectives, and aim to collaboratively minimize a global function by exchanging messages with their neighbors. Leveraging sparsification, gradient tracking and variance-reduction, we propose a novel communication-efficient decentralized optimization scheme that is suitable for resource-constrained time-varying directed networks. We prove that in the case of smooth and strongly-convex objective functions, the proposed scheme achieves an accelerated linear convergence rate. To our knowledge, this is the first decentralized optimization framework for time-varying directed networks that achieves such a convergence rate and applies to settings requiring sparsified communication. Experimental results on both synthetic and real datasets verify the theoretical results and demonstrate efficacy of the proposed scheme.

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BibTeXRIS

Yiyue Chen, Abolfazl Hashemi, Haris Vikalo. 2021-01-23. Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs. https://arxiv.org/abs/2101.09583

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