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Asynchronous Federated Optimization

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arxiv 1903.03934 v5 pith:TNYNQEQX submitted 2019-03-10 cs.DC cs.LG

Cong Xie , Sanmi Koyejo , Indranil Gupta This is my paper
classification cs.DCcs.LG
keywords federatedalgorithmasynchronousoptimizationproposedapplicationsapproachconvergence
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 27 Pith papers

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