Distributed averaging for accuracy prediction in networked systems

TL;DR

This paper introduces a heuristic method for nodes in distributed networks to estimate the convergence time of averaging algorithms, enhancing decision-making and network awareness in various applications.

Contribution

It proposes a distributed approach for predicting averaging convergence rates using local graph metrics, improving efficiency and adaptability in networked systems.

Findings

Enables nodes to estimate convergence time with minimal communication

Improves outlier detection and performance evaluation in dynamic networks

Provides insights into network structure and node roles

Abstract

Distributed averaging is among the most relevant cooperative control problems, with applications in sensor and robotic networks, distributed signal processing, data fusion, and load balancing. Consensus and gossip algorithms have been investigated and successfully deployed in multi-agent systems to perform distributed averaging in synchronous and asynchronous settings. This study proposes a heuristic approach to estimate the convergence rate of averaging algorithms in a distributed manner, relying on the computation and propagation of local graph metrics while entailing simple data elaboration and small message passing. The protocol enables nodes to predict the time (or the number of interactions) needed to estimate the global average with the desired accuracy. Consequently, nodes can make informed decisions on their use of measured and estimated data while gaining awareness of the global structure of the network, as well as their role in it. The study presents relevant applications to outliers identification and performance evaluation in switching topologies.