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Related Experiment Videos

MEC-Enabled Hierarchical Federated Learning for Resource-Aware Device Selection in IIoT.

Hu Tao1,2, Duan Li1, Bin Qiu1

  • 1School of Computer Science and Engineering, Guilin University of Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a device selection strategy for Hierarchical Federated Learning (HFL) in Industrial Internet of Things (IIoT) to improve model convergence stability. The proposed method enhances resource efficiency and training stability in dynamic edge computing environments.

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Electrical Engineering

Background:

  • Hierarchical Federated Learning (HFL) with Mobile Edge Computing (MEC) is promising for Industrial Internet of Things (IIoT) due to reduced communication overhead.
  • Dynamic device participation and varied training objectives in real-world IIoT hinder model convergence and system performance.

Purpose of the Study:

  • To propose a dynamic device selection strategy for HFL in IIoT to enhance model convergence stability.
  • To optimize system resource consumption and model performance under dynamic conditions.

Main Methods:

  • A device selection strategy based on task completion probability was developed for dynamic device participation.
  • An optimization objective was formulated to minimize the loss function under resource constraints.
Keywords:
device selectiondynamicshierarchical federated learning (HFL)industrial internet of things (IIoT)resource allocation

Related Experiment Videos

  • The objective was reformulated as a loss upper bound minimization problem and solved iteratively.

    • Main Results:

    • The proposed method demonstrated superior resource efficiency and training stability in simulations.
    • Compared to state-of-the-art HFL, the new method reduced average training delay by 18% and energy consumption by 22%.
    • Competitive model accuracy was maintained under dynamic IIoT conditions.

    Conclusions:

    • The joint optimization strategy effectively addresses challenges in dynamic HFL for IIoT.
    • The approach validates the feasibility of balancing resource efficiency, training stability, and model performance in practical IIoT applications.