  Linear regression is a fundamental and primitive problem in supervised
machine learning, with applications ranging from epidemiology to finance. In
this work, we propose methods for speeding up distributed linear regression. We
do so by leveraging randomized techniques, while also ensuring security and
straggler resiliency in asynchronous distributed computing systems.
Specifically, we randomly rotate the basis of the system of equations and then
subsample blocks, to simultaneously secure the information and reduce the
dimension of the regression problem. In our setup, the basis rotation
corresponds to an encoded encryption in an approximate gradient coding scheme,
and the subsampling corresponds to the responses of the non-straggling servers
in the centralized coded computing framework. This results in a distributive
iterative stochastic approach for matrix compression and steepest descent.
