Optimization-based Block Coordinate Gradient Coding

Existing gradient coding schemes introduce identical redundancy across the coordinates of gradients and hence cannot fully utilize the computation results from partial stragglers. This motivates the introduction of diverse redundancies across the coordinates of gradients. This paper considers a distributed computation system consisting of one master and
$N$
workers characterized by a general partial straggler model and focuses on solving a general large-scale machine learning problem with
$L$
model parameters. We show that it is sufficient to provide at most
$N$
levels of redundancies for tolerating
$0,1, \cdots, N-1$
stragglers, respectively. Consequently, we propose an optimal block coordinate gradient coding scheme based on a stochastic optimization problem that optimizes the partition of the
$L$
coordinates into
$N$
blocks, each with identical redundancy, to minimize the expected overall runtime for collaboratively computing the gradient. We obtain an optimal solution using a stochastic projected subgradient method and propose two low-complexity approximate solutions with closed-from expressions, for the stochastic optimization problem. We also show that under a shifted-exponential distribution, for any
$L$
, the expected overall runtimes of the two approximate solutions and the minimum overall runtime have sub-linear multiplicative gaps in
$N$
. To the best of our knowledge, this is the first work that optimizes the redundancies of gradient coding introduced across the coordinates of gradients.