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Creators/Authors contains: "McMahan, Brendan"

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  1. ; ; ; ; (, Proceedings of the 36th International Conference on Machine Learning, PMLR)
    We consider convex SGD updates with a block-cyclic structure, i.e., where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution. This situation arises, e.g., in Federated Learning where the mobile devices available for updates at different times during the day have different characteristics. We show that such block-cyclic structure can significantly deteriorate the performance of SGD, but propose a simple approach that allows prediction with the same guarantees as for i.i.d., non-cyclic, sampling. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, Advances in neural information processing systems)
    We suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph. We then use the framework and derive lower bounds for several specific parallel optimization settings, including delayed updates and parallel processing with intermittent communication. We highlight gaps between lower and upper bounds on the oracle complexity, and cases where the “natural” algorithms are not known to be optimal. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available