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Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly

Artin Spiridonoff1, Alex Olshevsky1, Ioannis Ch Paschalidis1

  • 1Division of Systems Engineering, Boston University, Boston, MA 02215, USA.

Journal of Machine Learning Research : JMLR
|September 29, 2020
PubMed
Summary

This study introduces a robust distributed optimization method for networks with unreliable communication. The modified Gradient-Push algorithm achieves performance comparable to centralized methods despite harsh network conditions and noisy gradients.

Keywords:
distributed optimizationstochastic gradient descent

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Area of Science:

  • Distributed Optimization
  • Networked Systems
  • Algorithm Analysis

Background:

  • Standard distributed optimization models assume reliable network conditions.
  • Harsh network environments introduce challenges like asynchronous updates, message delays, and data loss.
  • Existing methods may struggle to maintain performance under such adverse conditions.

Purpose of the Study:

  • To develop and analyze a modified Gradient-Push method for distributed optimization.
  • To address challenges posed by harsh network conditions including asynchronous updates, message delays, and data loss.
  • To evaluate the performance of the proposed method under noisy gradient conditions.

Main Methods:

  • Modification of the Gradient-Push algorithm for distributed optimization.
  • Analysis under a harsh network model with asynchronous updates, message delays, and message losses.
  • Assumptions include noisy gradient generation, strong convexity of the total function, and Lipschitz gradients for individual functions.

Main Results:

  • The proposed method demonstrates resilience to harsh network conditions and noisy gradients.
  • Asymptotic performance matches the bounds of centralized gradient descent using summed noisy gradients.
  • The algorithm effectively handles unreliable communication channels and asynchronous node operations.

Conclusions:

  • The modified Gradient-Push method offers a robust solution for distributed optimization in challenging network environments.
  • The findings suggest practical applicability in real-world distributed systems where network reliability is not guaranteed.
  • This work advances the understanding of distributed optimization under adversarial network conditions.