Distributed Newton method for regularized logistic regression

Regularized logistic regression is a very successful clas-sification 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 regression. Many interesting techniques are dis-cussed for reducing the communication cost. Experi-ments show that the proposed method is faster than state of the art approaches such as alternating direction method of multipliers (ADMM).