# Deep Learning-Based Average Consensus

arXiv: https://arxiv.org/abs/1908.09963 · HTML: https://mlsanity.com/paper/1908.09963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [AdaTerm: Adaptive T-Distribution Estimated Robust Moments for Noise-Robust Stochastic Gradient Optimization](https://mlsanity.com/paper/2201.06714/) (2022)
- [Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2212.12191/) (2022)

## Abstract (arXiv)

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.