  We devise a one-shot approach to distributed sparse regression in the
high-dimensional setting. The key idea is to average "debiased" or
"desparsified" lasso estimators. We show the approach converges at the same
rate as the lasso as long as the dataset is not split across too many machines.
We also extend the approach to generalized linear models.
