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arXiv · 2104.03649

Quantized Distributed Gradient Tracking Algorithm with Linear Convergence in Directed Networks

Yongyang Xiong·Ligang Wu·Keyou You·Lihua Xie
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Abstract

Communication efficiency is a major bottleneck in the applications of distributed networks. To address the problem, the problem of quantized distributed optimization has attracted a lot of attention. However, most of the existing quantized distributed optimization algorithms can only converge sublinearly. To achieve linear convergence, this paper proposes a novel quantized distributed gradient tracking algorithm (Q-DGT) to minimize a finite sum of local objective functions over directed networks. Moreover, we explicitly derive the update rule for the number of quantization levels, and prove that Q-DGT can converge linearly even when the exchanged variables are respectively one bit. Numerical results also confirm the efficiency of the proposed algorithm.

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Yongyang Xiong, Ligang Wu, Keyou You, Lihua Xie. 2021-04-08. Quantized Distributed Gradient Tracking Algorithm with Linear Convergence in Directed Networks. https://arxiv.org/abs/2104.03649

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