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Dominating Set Model Aggregation for communication-efficient decentralized deep learning.

Fateme Fotouhi1, Aditya Balu2, Zhanhong Jiang3

  • 1Department of Mechanical Engineering, Iowa State University, Ames, 50011, IA, USA; Department of Computer Science, Iowa State University, Ames, 50011, IA, USA.

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|December 13, 2023
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Summary
This summary is machine-generated.

This study introduces a new decentralized deep learning method, Minimum Connected Dominating Set Model Aggregation (DSMA), to significantly reduce communication overhead in peer-to-peer networks. DSMA achieves up to 100X faster communication while maintaining or improving model accuracy.

Keywords:
Communication-efficientConnected dominating setConvergence rateDecentralized learningDistributed deep learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Graph Theory

Background:

  • Decentralized deep learning relies on peer-to-peer communication of model parameters/gradients.
  • High communication overhead is a major challenge, particularly in harsh environments like underwater sensor networks.
  • Existing methods often prioritize accuracy over communication efficiency.

Purpose of the Study:

  • To reduce communication overhead in decentralized deep learning.
  • To maintain or improve model performance compared to state-of-the-art algorithms.
  • To introduce a novel algorithm, Minimum Connected Dominating Set Model Aggregation (DSMA).

Main Methods:

  • Applied graph theory concept of Minimum Connected Dominating Set (MCDS).
  • Developed a new decentralized deep learning algorithm: DSMA.
  • Investigated DSMA across various communication graph topologies, agent numbers, and neural network architectures.

Main Results:

  • Achieved significant reduction in communication time (up to 100X).
  • Preserved or enhanced model accuracy compared to existing methods.
  • Demonstrated algorithm convergence through analysis.

Conclusions:

  • DSMA effectively reduces communication overhead in decentralized deep learning.
  • The proposed method offers a practical solution for communication-intensive environments.
  • DSMA provides a viable alternative to current state-of-the-art algorithms with improved efficiency.