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A major hurdle in machine learning is scalability to massive datasets.Approaches to overcome this hurdle include compression of the data matrix anddistributing the computations. \textit{Leverage score sampling} provides acompressed approximation of a data matrix using an importance weighted subset.\textit{Gradient coding} has been recently proposed in distributed optimizationto compute the gradient using multiple unreliable worker nodes. By designingcoding matrices, gradient coded computations can be made resilient tostragglers, which are nodes in a distributed network that degrade systemperformance. We present a novel \textit{weighted leverage score} approach, thatachieves improved performance for distributed gradient coding by utilizing animportance sampling.

Weighted Gradient Coding with Leverage Score Sampling