Scholarlink

With the widespread adoption of intelligent devices such as Augmented Reality (AR) glasses and Unmanned Aerial Vehicles (UAVs) in power equipment inspection, data privacy and efficient on-device inference becomes increasingly important. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. However, naive model aggregation in FL often suffers from inefficiencies due to data and device heterogeneity, and the resulting models are typically too large-scale for deployment on resource-constrained terminals. To address these challenges, we propose FedCSAD, a hierarchical FL framework that combines Contextual Bandit-based Client pair Selection (CBCS) and Confidence-weighted Multi-teacher Knowledge Distillation (CMKD). CBCS optimizes aggregation by adaptively selecting high-quality model pairs using contextual information, while CMKD compresses global and edge models into compact student models suitable for terminal deployment. Experiments on CIFAR-10 and InsPLAD, a dataset based on real industrial data, demonstrate that FedCSAD outperforms existing methods in model accuracy, convergence speed, and deployment efficiency, offering a scalable and privacy-preserving solution for real-time intelligent inspection in the power industry.

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

FedCSAD: Federated Learning with Contextual Client Selection and Confidence-Weighted Multi-teacher Knowledge Distillation in Power Equipment Inspection

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Tianyang Lu,
  • Lihua Sun,
  • Chaolin Han,
  • Yubing Bao,
  • Bingzhuo Yu,
  • Zhen Wang,
  • Di Zhang,
  • Wei Liu

摘要

With the widespread adoption of intelligent devices such as Augmented Reality (AR) glasses and Unmanned Aerial Vehicles (UAVs) in power equipment inspection, data privacy and efficient on-device inference becomes increasingly important. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. However, naive model aggregation in FL often suffers from inefficiencies due to data and device heterogeneity, and the resulting models are typically too large-scale for deployment on resource-constrained terminals. To address these challenges, we propose FedCSAD, a hierarchical FL framework that combines Contextual Bandit-based Client pair Selection (CBCS) and Confidence-weighted Multi-teacher Knowledge Distillation (CMKD). CBCS optimizes aggregation by adaptively selecting high-quality model pairs using contextual information, while CMKD compresses global and edge models into compact student models suitable for terminal deployment. Experiments on CIFAR-10 and InsPLAD, a dataset based on real industrial data, demonstrate that FedCSAD outperforms existing methods in model accuracy, convergence speed, and deployment efficiency, offering a scalable and privacy-preserving solution for real-time intelligent inspection in the power industry.