We consider a scenario where the nodes of a network initially each have a vector
of observations, and their goal is to reach a consensus on the values of the $m$
largest values of the average vector. Selective gossip as a distributed
averaging algorithm which adaptively focuses communication resources on the
largest values of the average vector as they are being computed, so as to reduce
the resources consumed in calculating components which are not among the
largest. We illustrate the utility of selective gossip through an application to
distributed particle filtering, where each node obtains a measurement and
selective gossip is used to fuse the estimates while focusing communication
resources on computing the weights of particles with the largest posterior
likelihood.