We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner.Namely, we show how to estimate the Wasserstein distance between two samples stored and kept on different devices/clients whilst a central entity/server orchestrates the computations  (again, without having access to the samples). To achieve this feat, we take advantage of the geometric properties of the Wasserstein distance -- in particular, the triangle inequality --  and that of the associated {\em geodesics}: our algorithm, FedWad (for Federated Wasserstein Distance), iteratively approximates  the Wasserstein distance by manipulating and exchanging distributions from the  space of geodesics in lieu of the input samples.   In addition to establishing the convergence properties of FedWad,   we provide empirical results on federated coresets and federate    optimal transport dataset distance, that we respectively exploit for   building a novel federated model and for boosting performance of popular federated learning algorithms.
Federated Wasserstein Distance
Alain Rakotomamonjy Kimia Nadjahi Liva Ralaivola