  Distributed computing offers a high degree of flexibility to accommodate
modern learning constraints and the ever increasing size of datasets involved
in massive data issues. Drawing inspiration from the theory of distributed
computation models developed in the context of gradient-type optimization
algorithms, we present a consensus-based asynchronous distributed approach for
nonparametric online regression and analyze some of its asymptotic properties.
Substantial numerical evidence involving up to 28 parallel processors is
provided on synthetic datasets to assess the excellent performance of our
method, both in terms of computation time and prediction accuracy.
