Numerically Stable Binary Gradient Coding

TL;DR: In this paper, the authors present a binary gradient coding scheme for distributed computation of the gradient of an objective function in a distributed network, which avoids operations over real or complex numbers and is inherently numerically unstable.

Abstract: A major hurdle in machine learning is scalability to massive datasets. One approach to overcoming this is to distribute the computational tasks among several workers. Gradient coding has been recently proposed in distributed optimization to compute the gradient of an objective function using multiple, possibly unreliable, worker nodes. By designing distributed coded schemes, gradient coded computations can be made resilient to stragglers, nodes with longer response time compared to other nodes in a distributed network. Most such schemes rely on operations over the real or complex numbers and are inherently numerically unstable. We present a binary scheme which avoids such operations, thereby enabling numerically stable distributed computation of the gradient. Also, some restricting assumptions in prior work are dropped, and a more efficient decoding is given.