Distributed Gradient Descent with Coded Partial Gradient Computations

Emre Ozfatura; Sennur Ulukus; Deniz Gündüz

ICASSP 2019accepted0 citations

Distributed Gradient Descent with Coded Partial Gradient Computations

Emre Ozfatura, Sennur Ulukus, Deniz Gündüz

Abstract

Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling servers; and they are typically designed to recover the full gradient, and thus, cannot provide a balance between the accuracy of the gradient and per-iteration completion time. Here we introduce a hybrid approach, called coded partial gradient computation (CPGC), that benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and decoding complexity.

BibTeX

@inproceedings{icassp2019_distributedgradi,
  title = {Distributed Gradient Descent with Coded Partial Gradient Computations},
  author = {Emre Ozfatura and Sennur Ulukus and Deniz Gündüz},
  booktitle = {ICASSP 2019},
  year = {2019}
}