  This paper presents an asynchronous incremental aggregated gradient algorithm
and its implementation in a parameter server framework for solving regularized
optimization problems. The algorithm can handle both general convex (possibly
non-smooth) regularizers and general convex constraints. When the empirical
data loss is strongly convex, we establish linear convergence rate, give
explicit expressions for step-size choices that guarantee convergence to the
optimum, and bound the associated convergence factors. The expressions have an
explicit dependence on the degree of asynchrony and recover classical results
under synchronous operation. Simulations and implementations on commercial
compute clouds validate our findings.
