Scholarlink

<p>Federated learning (FL) is compelling for construction safety because sensitive, site-specific data can remain on devices; however, hyperparameter optimization (HPO) in FL is often impractical on resource-constrained edge nodes due to multi-round tuning and communication overhead. Existing FL-HPO approaches (e.g., weight sharing and server-only selection) either incur iterative client-server loops or underutilize client heterogeneity, thereby raising privacy and governance concerns. We propose an <i>asynchronous, one-shot</i> local Bayesian optimization on each client using a small data subset, followed by <i>loss-weighted aggregation</i> of client-proposed hyperparameters on the server to produce a single global set for collaborative training. The pipeline is training-method agnostic and integrated into a Digital Twin (DT) platform for device registration, orchestration, and telemetry. Across public datasets (MNIST, EEGEyeState, Weather) and a private worker-safety dataset, our approach improves convergence and accuracy/MAE relative to simple averaging and reduces response time on Raspberry Pi clients, yielding an average improvement of 6.44% in end-to-end performance while remaining deployable on constrained AIoT hardware. In safety prediction, faster convergence and lower error enable earlier and more reliable hazard indicators, with on-device inference supporting near–real-time alerts to supervisors and wearables. We also discuss scalability, privacy of shared hyperparameters, and task complexity as directions for future work in safety-critical FL.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimizing Federated Learning based on Weighted Hyperparameter Aggregation in Energy-constrained Building Construction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

查看全文
  • Sa Jim Soe Moe,
  • Anam Nawaz Khan,
  • Anh Tuan Nguyen,
  • Do Hyeun Kim

摘要

Federated learning (FL) is compelling for construction safety because sensitive, site-specific data can remain on devices; however, hyperparameter optimization (HPO) in FL is often impractical on resource-constrained edge nodes due to multi-round tuning and communication overhead. Existing FL-HPO approaches (e.g., weight sharing and server-only selection) either incur iterative client-server loops or underutilize client heterogeneity, thereby raising privacy and governance concerns. We propose an asynchronous, one-shot local Bayesian optimization on each client using a small data subset, followed by loss-weighted aggregation of client-proposed hyperparameters on the server to produce a single global set for collaborative training. The pipeline is training-method agnostic and integrated into a Digital Twin (DT) platform for device registration, orchestration, and telemetry. Across public datasets (MNIST, EEGEyeState, Weather) and a private worker-safety dataset, our approach improves convergence and accuracy/MAE relative to simple averaging and reduces response time on Raspberry Pi clients, yielding an average improvement of 6.44% in end-to-end performance while remaining deployable on constrained AIoT hardware. In safety prediction, faster convergence and lower error enable earlier and more reliable hazard indicators, with on-device inference supporting near–real-time alerts to supervisors and wearables. We also discuss scalability, privacy of shared hyperparameters, and task complexity as directions for future work in safety-critical FL.