Distributed Newton method for regularized logistic regression

Abstract. Regularized logistic regression is a very useful classification method, but for large-scale data, its distributed training has not been investigated much. In this work, we propose a distributed Newton method for training logistic re-gression. Many interesting techniques are discussed for reducing the communi-cation cost and speeding up the computation. Experiments show that the proposed method is competitive with or even faster than state-of-the-art approaches such as Alternating Direction Method of Multipliers (ADMM) and Vowpal Wabbit (VW). We have released an MPI-based implementation for public use.