Approximate Gradient Coding Using Convex Optimization

In distributed large scale ML training, the problem is typically one of minimizing the empirical risk, a loss function $L$ that depends on the training dataset with respect to a $d$-length parameter vector. Workers are assigned portions of the original dataset and at each iteration of the algorithm,... View More