Deep Learning-Based Average Consensus
Abstract
In this study, we analyzed the problem of accelerating the linear average consensus algorithm for complex networks. We propose a data-driven approach to tuning the weights of temporal (i.e., time-varying) networks using deep learning techniques. Given a finite-time window, the proposed approach first unfolds the linear average consensus protocol to obtain a feedforward signal-flow graph, which is regarded as a neural network. The edge weights of the obtained neural network are then trained using standard deep learning techniques to minimize consensus error over a given finite-time window. Through this training process, we obtain a set of optimized time-varying weights, which yield faster consensus for a complex network. We also demonstrate that the proposed approach can be extended for infinite-time window problems. Numerical experiments revealed that our approach can achieve a significantly smaller consensus error compared to baseline strategies.
Cite
@article{arxiv.1908.09963,
title = {Deep Learning-Based Average Consensus},
author = {Masako Kishida and Masaki Ogura and Yuichi Yoshida and Tadashi Wadayama},
journal= {arXiv preprint arXiv:1908.09963},
year = {2023}
}