Distributed Averaging for Accuracy Prediction in Networked Systems

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. Gossip algorithms have been investigated and successfully deployed in multi-agent systems to perform distributed averaging in 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 proposed strategy enables nodes to predict 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. The study presents applications to outliers identification and performance evaluation in switching topologies.