# Asynchronous Gossip for Averaging and Spectral Ranking

arXiv: https://arxiv.org/abs/1309.7841 · HTML: https://mlsanity.com/paper/1309.7841/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2013.

## Built on by (most cited first, 1 of 1)

- [Online Multi-Agent Decentralized Byzantine-robust Gradient Estimation](https://mlsanity.com/paper/2209.15274/) (2022)

## Abstract (arXiv)

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.