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Decentralized ADMM with compressed and event-triggered communication.

Zhen Zhang1, Shaofu Yang1, Wenying Xu2

  • 1School of Computer Science and Engineering, Southeast University, 211189, Nanjing, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 19, 2023
PubMed
Summary

This study introduces a novel decentralized optimization algorithm (CC-DQM) that enhances communication efficiency in networks. It achieves linear convergence by using event-triggered and compressed communication for decentralized optimization problems.

Keywords:
ADMMDecentralized optimizationEfficient communicationSecond-order algorithms

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Area of Science:

  • Optimization Theory
  • Distributed Systems
  • Networked Control Systems

Background:

  • Decentralized optimization problems involve multiple agents cooperating to minimize a global objective function.
  • Existing methods often face challenges with communication overhead and computational complexity.

Purpose of the Study:

  • To propose a novel, communication-efficient decentralized optimization algorithm.
  • To reduce communication and computation costs in decentralized optimization.

Main Methods:

  • Developed a decentralized second-order algorithm, CC-DQM (communication-censored and communication-compressed quadratically approximated ADMM).
  • Combined event-triggered communication with compressed communication.
  • Scheduled Hessian updates based on trigger conditions to reduce computation.

Main Results:

  • CC-DQM maintains exact linear convergence despite compression errors and intermittent communication.
  • Theoretical analysis confirms convergence properties for strongly convex and smooth objective functions.
  • Numerical experiments demonstrated significant communication efficiency.

Conclusions:

  • The proposed CC-DQM algorithm effectively addresses communication and computation challenges in decentralized optimization.
  • Event-triggered and compressed communication strategies are viable for improving efficiency.
  • The algorithm shows promise for large-scale networked systems requiring efficient optimization.