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<p>The Artificial Intelligence of Things (AIoT) ecosystem faces significant challenges related to limited client energy budgets and resource heterogeneity, particularly when employing the Federated Learning (FL) framework. This paper presents a novel energy-efficient client selection algorithm for FL, designed to address these challenges by integrating Wireless Power Transfer (WPT), where WPT involves in the client selection optimization, based on real-time energy availability and resource heterogeneity. We formulate the client selection problem as a multi-dimensional knapsack problem (MKP) and solve it using dynamic programming to maximize energy efficiency while maintaining fast convergence. Experimental results show that incorporating WPT leads to a reduction in unit energy consumption by over 24.54%; while, the proposed algorithm achieves a reduction of over 15.31% compared to random selection. The proposed approach improves energy utilization, demonstrates strong resilience to client heterogeneity, and adapts efficiently to varying energy supply conditions.</p>

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Eecs-fl: energy-efficient client selection for federated learning in AIoT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

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  • Yiyang Zhang,
  • Yiming Luo,
  • Tao Yang,
  • Xiaofeng Wu,
  • Bo Hu

摘要

The Artificial Intelligence of Things (AIoT) ecosystem faces significant challenges related to limited client energy budgets and resource heterogeneity, particularly when employing the Federated Learning (FL) framework. This paper presents a novel energy-efficient client selection algorithm for FL, designed to address these challenges by integrating Wireless Power Transfer (WPT), where WPT involves in the client selection optimization, based on real-time energy availability and resource heterogeneity. We formulate the client selection problem as a multi-dimensional knapsack problem (MKP) and solve it using dynamic programming to maximize energy efficiency while maintaining fast convergence. Experimental results show that incorporating WPT leads to a reduction in unit energy consumption by over 24.54%; while, the proposed algorithm achieves a reduction of over 15.31% compared to random selection. The proposed approach improves energy utilization, demonstrates strong resilience to client heterogeneity, and adapts efficiently to varying energy supply conditions.