Distributed block-diagonal approximation methods for regularized empirical risk minimization
Distributed block-diagonal approximation methods for regularized empirical risk minimization
Abstract In recent years, there is a growing need to train machine learning models on a huge volume of data. Therefore, designing efficient distributed optimization algorithms for empirical risk minimization (ERM) has become an active and challenging research topic. In this paper, we propose a flexible framework for distributed ERM …