  Solving logistic regression with L1-regularization in distributed settings is
an important problem. This problem arises when training dataset is very large
and cannot fit the memory of a single machine. We present d-GLMNET, a new
algorithm solving logistic regression with L1-regularization in the distributed
settings. We empirically show that it is superior over distributed online
learning via truncated gradient.
