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Coding for Large-Scale Distributed Machine Learning.

Ming Xiao1, Mikael Skoglund1

  • 1Division of Information Science and Engineering, Royal Institute of Technology, Malvinas Vag 10, KTH, 100-44 Stockholm, Sweden.

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Summary
This summary is machine-generated.

This review explores coding techniques for large-scale distributed machine learning (DML) to enhance efficiency and reliability. It covers gradient coding and random coding for primal-based DML and proposes new methods for primal-dual-based DML.

Keywords:
ADMMerror-control codinggradient codingrandom codes

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Area of Science:

  • Computer Science
  • Information Theory
  • Machine Learning

Background:

  • Machine learning is increasingly distributed due to large data volumes and sensor networks.
  • Large-scale distributed machine learning (DML) faces challenges like delay, errors, and efficiency.
  • Existing solutions include error-control and performance-boosting schemes, with recent focus on error-control coding.

Purpose of the Study:

  • To provide a comprehensive review of coding principles and recent developments for large-scale DML.
  • To introduce theories and algorithms for applying coding to DML systems.
  • To address the lack of surveys on coding for large-scale learning.

Main Methods:

  • Review of gradient coding with optimal code distance for primal-based DML.
  • Introduction to random coding for gradient-based DML.
  • Proposal of a separate coding method for the two steps in primal-dual-based DML (ADMM).

Main Results:

  • Coding offers benefits for DML, including high efficiency and low complexity.
  • Specific coding schemes are discussed for different steps in distributed optimization (primal-based and primal-dual-based).
  • The review highlights the potential of error-control coding to improve DML reliability.

Conclusions:

  • Coding is a promising approach to tackle challenges in large-scale DML.
  • Further research directions are identified for advancing coding techniques in DML.
  • This work provides a foundational understanding for future developments in efficient and reliable DML.