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We present ErasureHead, a new approach for distributed gradient descent (GD)that mitigates system delays by employing approximate gradient coding. Gradientcoded distributed GD uses redundancy to exactly recover the gradient at eachiteration from a subset of compute nodes. ErasureHead instead uses approximategradient codes to recover an inexact gradient at each iteration, but withhigher delay tolerance. Unlike prior work on gradient coding, we provide aperformance analysis that combines both delay and convergence guarantees. Weestablish that down to a small noise floor, ErasureHead converges as quickly asdistributed GD and has faster overall runtime under a probabilistic delaymodel. We conduct extensive experiments on real world datasets and distributedclusters and demonstrate that our method can lead to significant speedups overboth standard and gradient coded GD.

ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding