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

TL;DR

This paper introduces DELCO, a decentralized ensemble learning method that uses Gaussian copulas to aggregate classifier predictions, reducing network load while maintaining accuracy and robustness.

Contribution

The paper presents a novel decentralized ensemble learning approach using Gaussian copulas for model aggregation, with a focus on network load constraints.

Findings

Competitive accuracy with centralized methods

Enhanced robustness with dependent classifiers

Effective model aggregation under network constraints

Abstract

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. 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 in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELCO