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<p>In Hierarchical Federated Learning, Split Learning is introduced to support cooperative training among resource constrained devices and edge servers. While high delay cost is a critical issue that needs to be addressed during model training, excessive energy consumption poses a fundamental threat to the system. In this paper, we investigate the problem of minimizing the total delay cost under the constraint of the energy cost for cooperation between nodes. We propose an online cooperative task allocation algorithm OTNA based on Lyapunov optimization theory, which can dynamically changing capabilities and resource states of heterogeneous devices, and coordinate them efficiently to improve device-side model training performance. We prove that there is an upper bound of OTNA through theoretical analysis. We also evaluate the performance of the algorithm through several experiments. Compared with SFL and RNCA, it is found that OTNA can save delay cost up to 18.83% and 17.02%, respectively.</p>

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Optimization for dynamic node cooperation in hierarchical split federated learning

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Zhuo Li,
  • Jingbo Dun,
  • Sailan Zou

摘要

In Hierarchical Federated Learning, Split Learning is introduced to support cooperative training among resource constrained devices and edge servers. While high delay cost is a critical issue that needs to be addressed during model training, excessive energy consumption poses a fundamental threat to the system. In this paper, we investigate the problem of minimizing the total delay cost under the constraint of the energy cost for cooperation between nodes. We propose an online cooperative task allocation algorithm OTNA based on Lyapunov optimization theory, which can dynamically changing capabilities and resource states of heterogeneous devices, and coordinate them efficiently to improve device-side model training performance. We prove that there is an upper bound of OTNA through theoretical analysis. We also evaluate the performance of the algorithm through several experiments. Compared with SFL and RNCA, it is found that OTNA can save delay cost up to 18.83% and 17.02%, respectively.