  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. \textit{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
\textit{stragglers}, nodes with longer response time comparing 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.
