Computing separable functions via gossip
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Finite-time convergent gradient flows with applications to network consensus
Automatica (Journal of IFAC)
IEEE Transactions on Information Theory
Distributed function calculation and consensus using linear iterative strategies
IEEE Journal on Selected Areas in Communications
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We propose a randomized algorithm aimed at reaching, in finite time, exact consensus among a set of agents that are linked though a connected, possibly time-varying, graph. The information exchanged among neighboring agents is limited in size, by randomly selecting the content from an agent internal state. The time needed to reach the agreement is a random variable, whose empirical cumulative distribution function is utilized to determine a stopping rule that ensures consensus is achieved with a prescribed confidence level. Simulation results show that the consensus-reaching time obtained with a prescribed confidence level compares relatively well to local-averaging-based algorithms. The algorithm is shown to be robust, to some extent, to lossy communications and is demonstrated on a weapon-target assignment problem.