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This paper targets solving distributed machine learning problems such as federated learning in a communication-efficient fashion.
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Local sgd converges fast and communicates little
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On the computation and communication complexity of parallel SGD with dynamic batch sizes for stochastic non-convex optimization
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
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