  Federated learning enables training on a massive number of edge devices. To
improve flexibility and scalability, we propose a new asynchronous federated
optimization algorithm. We prove that the proposed approach has near-linear
convergence to a global optimum, for both strongly convex and a restricted
family of non-convex problems. Empirical results show that the proposed
algorithm converges quickly and tolerates staleness in various applications.
