Communication-Efficient Distributed Optimization using an Approximate Newton-type Method
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Abstract
We present a novel Newton-type method for distributed optimization, which is particularly wel suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably improves with the data size, requiring an essentially constant number of iterations under reasonable assumptions. We provide theoretical and empirical evidence of the advantages of our method compared to other approaches, such as one-shot parameter averaging and ADMM.
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