Section: New Results

Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

Participant : Benjamin Guedj.

We examine a network of learners which address the same classification task but must learn from different data sets. The learners can share a limited portion of their data sets so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach in which the shared data and the trained models are sent to a central machine that allows to build an ensemble of classifiers. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness as compared to gold standard approaches. A companion python implementation can be downloaded at https://github.com/john-klein/DELCO.

Joint work with John Klein, Olivier Colot, Mahmoud Albardan (all from CRIStAL lab, UMR 9189, Univ. Lille. Preprint submitted: [50].