Often distributed information is accumulated over a population of neurons,
agents and sites, leading to the study of estimation approaches that are based
on  local computation and information sharing. In this talk, I
will focus on a network of observers making low signal-to-noise observations
concerning an unknown vector. I will show that the mean square error based on
local computations and information sharing, while being ignorant about network
topology and global noise structure, can be very close to the error of
centralized maximum likelihood estimation.