  This paper studies the problem of finding the median of N distinct numbers
distributed across networked agents. Each agent updates its estimate for the
median from noisy local observations of one of the N numbers and information
from neighbors. We consider an undirected random network that is connected on
average, and a noisy observation sequence that has finite variance and almost
surely decaying bias. We present a consensus+innovations algorithm with clipped
innovations. Under some regularity assumptions on the network and observation
model, we show that each agent's local estimate converges to the set of
median(s) almost surely at an asymptotic sublinear rate. Numerical experiments
demonstrate the effectiveness of the presented algorithm.
