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A Graph Neural Network Based Decentralized Learning Scheme.

Huiguo Gao1,2, Mengyuan Lee1,2, Guanding Yu1,2

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

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This study introduces a novel decentralized learning scheme using distributed parallel stochastic gradient descent (DPSGD) and graph neural networks (GNNs). The method enhances model performance and robustness, even with non-iid data and network issues.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Decentralized learning is crucial for data privacy and transmission efficiency, using distributed user data for global models.
  • Challenges include link loss, partial device participation, and non-independent and identically distributed (non-iid) data, which degrade performance.
  • Existing methods often struggle with non-iid data or are limited to linear models.

Purpose of the Study:

  • To propose a robust decentralized learning scheme addressing challenges like non-iid data, link loss, and partial device participation.
  • To improve the performance and convergence of decentralized learning algorithms.

Main Methods:

  • A novel decentralized learning scheme combining distributed parallel stochastic gradient descent (DPSGD) and graph neural networks (GNNs).
  • Each device computes local stochastic gradients and updates its local model.
  • Devices use GNNs to exchange model parameters with neighbors for global model averaging.

Main Results:

  • The proposed algorithm demonstrates convergence to near-optimal results across both iid and non-iid data distributions.
  • The scheme shows significant robustness against link loss and partial device participation.
  • Validation through extensive simulation results confirms the algorithm's effectiveness.

Conclusions:

  • The DPSGD and GNN-based decentralized learning scheme effectively overcomes key challenges in distributed learning.
  • The approach offers a robust and efficient solution for acquiring global models from decentralized data.
  • This work advances decentralized learning, particularly for scenarios with heterogeneous data and unreliable network conditions.