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<p>This paper proposes a finite-time distributed semi-supervised learning (FTDSSL) algorithm based on the zero-gradient-sum strategy, manifold regularization, and extreme learning machines. The FTDSSL algorithm is designed for addressing distributed learning problems involving distributed data, including unlabeled samples. Thanks to the ZGS strategy, it yields results comparable to semi-supervised learning algorithms using single-layer feedforward neural networks on the full dataset. Moreover, FTDSSL exhibits convergence within a finite number of iterations. Each FTDSSL iteration shares only updated output weights between neighbors, preserving data privacy and communication bandwidth. Theoretical underpinnings, as exemplified by Theorem 1, demonstrate the global convergence of the FTDSSL algorithm through Lyapunov theory. Compared to event-triggered distributed semi-supervised learning, FTDSSL significantly reduces iteration count rather than communication volume. Experimental results validate that the FTDSSL algorithm converges within a finite time frame and proves efficient for distributed learning, particularly on datasets that include unlabeled samples.</p>

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Manifold regularization based finite time distributed semi-supervised learning algorithm

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Xinyi Li,
  • Lele Yue,
  • Jin Xie,
  • Sanyang Liu,
  • Fei Gao

摘要

This paper proposes a finite-time distributed semi-supervised learning (FTDSSL) algorithm based on the zero-gradient-sum strategy, manifold regularization, and extreme learning machines. The FTDSSL algorithm is designed for addressing distributed learning problems involving distributed data, including unlabeled samples. Thanks to the ZGS strategy, it yields results comparable to semi-supervised learning algorithms using single-layer feedforward neural networks on the full dataset. Moreover, FTDSSL exhibits convergence within a finite number of iterations. Each FTDSSL iteration shares only updated output weights between neighbors, preserving data privacy and communication bandwidth. Theoretical underpinnings, as exemplified by Theorem 1, demonstrate the global convergence of the FTDSSL algorithm through Lyapunov theory. Compared to event-triggered distributed semi-supervised learning, FTDSSL significantly reduces iteration count rather than communication volume. Experimental results validate that the FTDSSL algorithm converges within a finite time frame and proves efficient for distributed learning, particularly on datasets that include unlabeled samples.