Non-Bayesian Social Learning on Random Digraphs With Aperiodically Varying Network Connectivity

Parasnis, Rohit; Franceschetti, Massimo; Touri, Behrouz

doi:10.1109/tcns.2022.3163670

Cited by 6 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Distributed averaging is a central mechanism to information mixing in distributed optimization [1,2], distributed parameter estimation and signal processing [3][4][5][6][7][8][9], decentralized control of robotic networks [10], and opinion dynamics [11][12][13][14][15][16]. Hence, a variety of distributed averaging dynamics have been studied till date within different mathematical frameworks [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Random Adaptation Perspective on Distributed Averaging

Parasnis

Verma

Franceschetti

et al. 2023

IEEE Control Syst. Lett.

Self Cite

View full text Add to dashboard Cite

We propose a random adaptation variant of time-varying distributed averaging dynamics in discrete time. We show that this leads to novel interpretations of fundamental concepts in distributed averaging, opinion dynamics, and distributed learning. Namely, we show that the ergodicity of a stochastic chain is equivalent to the almost sure (a.s.) finite-time agreement attainment in the proposed random adaptation dynamics. Using this result, we provide a new interpretation for the absolute probability sequence of an ergodic chain. We then modify the base-case dynamics into a time-reversed inhomogeneous Markov chain, and we show that in this case ergodicity is equivalent to the uniqueness of the limiting distributions of the Markov chain. Finally, we introduce and study a time-varying random adaptation version of the Friedkin-Johnsen model and a rank-one perturbation of the base-case dynamics.

show abstract