  We consider a collaborative learning setting where the goal of each agent is
to improve their own model by leveraging the expertise of collaborators, in
addition to their own training data. To facilitate the exchange of expertise
among agents, we propose a distillation-based method leveraging shared
unlabeled auxiliary data, which is pseudo-labeled by the collective. Central to
our method is a trust weighting scheme that serves to adaptively weigh the
influence of each collaborator on the pseudo-labels until a consensus on how to
label the auxiliary data is reached. We demonstrate empirically that our
collaboration scheme is able to significantly boost the performance of
individual models in the target domain from which the auxiliary data is
sampled. By design, our method adeptly accommodates heterogeneity in model
architectures and substantially reduces communication overhead compared to
typical collaborative learning methods. At the same time, it can provably
mitigate the negative impact of bad models on the collective.
