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<p>In earth exploration and coastal monitoring, salt-spray prediction (SSP) plays a pivotal role in assessing corrosion risks and evaluating environmental impacts on infrastructure and ecosystems. In industrial Internet of Things (IIoT) networks, SSP relies on geographically distributed sensing devices to collect and process large volumes of environmental data. However, conventional centralized SSP solutions are difficult to deploy over wide-area IIoT infrastructures and often incur substantial communication delays, limiting their real-time applicability. To address these challenges, this paper investigates latency-constrained optimization in federated learning (FL) for SSP within IIoT networks, aiming to enhance communication efficiency while minimizing overall model training latency. We propose two adaptive wireless bandwidth allocation strategies: one based on instantaneous channel state information (I-CSI) and the other on statistical channel state information (S-CSI). The I-CSI-based method dynamically allocates bandwidth according to real-time channel conditions, enabling rapid convergence and high predictive accuracy in relatively stable IIoT wireless links. In contrast, the S-CSI-based method leverages long-term channel statistics to provide robust performance in fast-varying or unpredictable IIoT environments. Extensive simulation results demonstrate that both strategies significantly reduce system latency, increase the number of active participating clients, and effectively balance convergence speed, accuracy, and bandwidth utilization. Notably, the I-CSI approach achieves faster convergence and higher accuracy under stable conditions, while the S-CSI approach offers steady improvements in highly dynamic IIoT scenarios. These findings underscore the critical role of intelligent bandwidth allocation in FL-enabled SSP systems and provide practical insights for optimizing communication resources in real-world IIoT deployments.</p>

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Wireless federated learning for salt-spray prediction in industrial IoT networks with delay constraint

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • Lincong Chen,
  • Jun Wang,
  • Jiaxu Duan,
  • Hui Li,
  • Xiaolin Chen,
  • Xinran Li,
  • Chao Ma

摘要

In earth exploration and coastal monitoring, salt-spray prediction (SSP) plays a pivotal role in assessing corrosion risks and evaluating environmental impacts on infrastructure and ecosystems. In industrial Internet of Things (IIoT) networks, SSP relies on geographically distributed sensing devices to collect and process large volumes of environmental data. However, conventional centralized SSP solutions are difficult to deploy over wide-area IIoT infrastructures and often incur substantial communication delays, limiting their real-time applicability. To address these challenges, this paper investigates latency-constrained optimization in federated learning (FL) for SSP within IIoT networks, aiming to enhance communication efficiency while minimizing overall model training latency. We propose two adaptive wireless bandwidth allocation strategies: one based on instantaneous channel state information (I-CSI) and the other on statistical channel state information (S-CSI). The I-CSI-based method dynamically allocates bandwidth according to real-time channel conditions, enabling rapid convergence and high predictive accuracy in relatively stable IIoT wireless links. In contrast, the S-CSI-based method leverages long-term channel statistics to provide robust performance in fast-varying or unpredictable IIoT environments. Extensive simulation results demonstrate that both strategies significantly reduce system latency, increase the number of active participating clients, and effectively balance convergence speed, accuracy, and bandwidth utilization. Notably, the I-CSI approach achieves faster convergence and higher accuracy under stable conditions, while the S-CSI approach offers steady improvements in highly dynamic IIoT scenarios. These findings underscore the critical role of intelligent bandwidth allocation in FL-enabled SSP systems and provide practical insights for optimizing communication resources in real-world IIoT deployments.