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Asynchronous Distributed Semi-Stochastic Gradient Optimization
With the recent proliferation of large-scale learning problems,there have
been a lot of interest on distributed machine learning algorithms, particularly
those that are based on stochastic gradient descent (SGD) and its variants.
However, existing algorithms either suffer from slow convergence due to the
inherent variance of stochastic gradients, or have a fast linear convergence
rate but at the expense of poorer solution quality. In this paper, we combine
their merits by proposing a fast distributed asynchronous SGD-based algorithm
with variance reduction. A constant learning rate can be used, and it is also
guaranteed to converge linearly to the optimal solution. Experiments on the
Google Cloud Computing Platform demonstrate that the proposed algorithm
outperforms state-of-the-art distributed asynchronous algorithms in terms of
both wall clock time and solution quality
Local SGD Converges Fast and Communicates Little
Mini-batch stochastic gradient descent (SGD) is state of the art in large
scale distributed training. The scheme can reach a linear speedup with respect
to the number of workers, but this is rarely seen in practice as the scheme
often suffers from large network delays and bandwidth limits. To overcome this
communication bottleneck recent works propose to reduce the communication
frequency. An algorithm of this type is local SGD that runs SGD independently
in parallel on different workers and averages the sequences only once in a
while.
This scheme shows promising results in practice, but eluded thorough
theoretical analysis. We prove concise convergence rates for local SGD on
convex problems and show that it converges at the same rate as mini-batch SGD
in terms of number of evaluated gradients, that is, the scheme achieves linear
speedup in the number of workers and mini-batch size. The number of
communication rounds can be reduced up to a factor of T^{1/2}---where T denotes
the number of total steps---compared to mini-batch SGD. This also holds for
asynchronous implementations. Local SGD can also be used for large scale
training of deep learning models.
The results shown here aim serving as a guideline to further explore the
theoretical and practical aspects of local SGD in these applications.Comment: to appear at ICLR 2019, 19 page
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