Work

Distributed Gradient Descent with Coded Partial Gradient Computations

Year: 2019

Type: conference-paper

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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2019.8683267

Authors: E. Ozfatura, S. Ulukus, D. Gündüz

Institutions: Imperial College London, University of Maryland, College Park

Language: English

Topic: Stochastic Gradient Optimization Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2901745114

FWCI: 2.61

Cites: 23

Cited by: 40

Related to: 10