  Collaborative learning has recently achieved very significant results. It
still suffers, however, from several issues, including the type of information
that needs to be exchanged, the criteria for stopping and how to choose the
right collaborators. We aim in this paper to improve the quality of the
collaboration and to resolve these issues via a novel approach inspired by
Optimal Transport theory. More specifically, the objective function for the
exchange of information is based on the Wasserstein distance, with a
bidirectional transport of information between collaborators. This formulation
allows to learns a stopping criterion and provide a criterion to choose the
best collaborators. Extensive experiments are conducted on multiple data-sets
to evaluate the proposed approach.
