Selective information exchange in collaborative clustering using regularized Optimal Transport

TL;DR

This paper introduces a novel collaborative clustering method using regularized Optimal Transport, which improves information exchange, automatically determines when to stop collaboration, and selects the best collaborators based on Wasserstein distance.

Contribution

It proposes a new approach leveraging Optimal Transport theory for selective information exchange, addressing key issues in collaborative learning.

Findings

Effective in improving collaboration quality

Automatically learns stopping criteria

Selects optimal collaborators based on Wasserstein distance

Abstract

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.