Asynchronous Federated Optimization [article]

Cong Xie, Sanmi Koyejo, Indranil Gupta
2020 arXiv   pre-print
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.
arXiv:1903.03934v5 fatcat:wzti5vo4dne2pdnara5rgvwsvi