  In many distributed learning problems, the heterogeneous loading of computing
machines may harm the overall performance of synchronous strategies. In this
paper, we propose an effective asynchronous distributed framework for the
minimization of a sum of smooth functions, where each machine performs
iterations in parallel on its local function and updates a shared parameter
asynchronously. In this way, all machines can continuously work even though
they do not have the latest version of the shared parameter. We prove the
convergence of the consistency of this general distributed asynchronous method
for gradient iterations then show its efficiency on the matrix factorization
problem for recommender systems and on binary classification.
