Collaborative Learning via Prediction Consensus. (arXiv:2305.18497v2 [cs.LG] UPDATED)

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 …

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