Asynchronous Federated Optimization
Cong Xie, Oluwasanmi O. Koyejo, Indranil Sen Gupta · arXiv (Cornell University) · 2019
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.