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arXiv · 1903.03934

Asynchronous Federated Optimization

Cong Xie·Sanmi Koyejo·Indranil Gupta
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Abstract

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.

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Cong Xie, Sanmi Koyejo, Indranil Gupta. 2019-03-10. Asynchronous Federated Optimization. https://arxiv.org/abs/1903.03934

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