  This paper studies the problem of distributed stochastic optimization in an
adversarial setting where, out of the $m$ machines which allegedly compute
stochastic gradients every iteration, an $\alpha$-fraction are Byzantine, and
can behave arbitrarily and adversarially. Our main result is a variant of
stochastic gradient descent (SGD) which finds $\varepsilon$-approximate
minimizers of convex functions in $T = \tilde{O}\big( \frac{1}{\varepsilon^2 m}
+ \frac{\alpha^2}{\varepsilon^2} \big)$ iterations. In contrast, traditional
mini-batch SGD needs $T = O\big( \frac{1}{\varepsilon^2 m} \big)$ iterations,
but cannot tolerate Byzantine failures. Further, we provide a lower bound
showing that, up to logarithmic factors, our algorithm is
information-theoretically optimal both in terms of sampling complexity and time
complexity.

Machine Learning;Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms;Optimization and Control;Machine Learning