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Related Experiment Video

Updated: Nov 12, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning.

Shi Pu1, Alex Olshevsky2, Ioannis Ch Paschalidis2

  • 1Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen, China and Shenzhen Research Institute of Big Data. The research was conducted when the author was with Division of Systems Engineering, Boston University, Boston, MA.

IEEE Signal Processing Magazine
|March 22, 2021
PubMed
Summary

Recent machine learning research overcomes barriers in distributed optimization. Distributed methods can now achieve network independence, matching centralized performance for faster model training.

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Area of Science:

  • Machine Learning
  • Distributed Systems
  • Optimization Theory

Background:

  • Distributed stochastic optimization is crucial for large-scale machine learning.
  • A key challenge is achieving performance comparable to centralized methods.
  • The asymptotic network independence property addresses this challenge.

Purpose of the Study:

  • To discuss recent advancements in distributed stochastic optimization.
  • To explain the asymptotic network independence property.
  • To analyze the performance of distributed versus centralized methods in machine learning.

Main Methods:

  • Review of recent theoretical results in distributed optimization.
  • Explanation of asymptotic network independence with an ML training example.
  • Mathematical comparison of distributed stochastic gradient descent (DSGD) and centralized stochastic gradient descent (SGD).

Main Results:

  • Certain distributed methods can overcome previous performance barriers.
  • Asymptotic network independence allows distributed methods to match centralized convergence rates.
  • DSGD performance can be comparable to SGD under specific network conditions.

Conclusions:

  • Distributed stochastic optimization is becoming increasingly efficient.
  • The asymptotic network independence property is a key enabler for high-performance distributed ML.
  • Further research can optimize distributed training strategies for enhanced scalability and speed.