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Federated learning has emerged as a key privacy-preserving paradigm for training models on decentralized data. However, the inherent heterogeneity of edge devices in terms of computational power and energy resources poses a significant challenge, leading to prohibitive energy consumption and reduced training efficiency. Existing studies are predicated on an idealized assumption that all client devices are homogeneous, ignoring the profound impact of device heterogeneity on energy consumption. To address this issue, we propose FedLay, a novel hierarchical federated training framework. FedLay introduces a client-leveling mechanism that organizes devices into a three-tier structure, mitigating the high energy costs associated with data transmission. At its core, an adaptive strategy dynamically manages local training efforts, ensuring that even devices with limited power can contribute effectively without compromising the overall learning objective. Extensive experiments conducted on real-world datasets demonstrate the efficiency of our FedLay. Compared to baseline methods, FedLay can significantly reduce the energy consumption of device transmission by approximately \(30.84\%\) , while maintaining competitive model performance.

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FedLay: An Energy-Efficient Hierarchical Federated Learning Framework for Heterogeneous Edge Devices

  • 会议论文

  • 发表日期：

    2026年

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  • Zhuopu Zhang,
  • Renqi Zhu,
  • Zongyang Yuan,
  • Jiaqi Li,
  • Lailong Luo,
  • Deke Guo

摘要

Federated learning has emerged as a key privacy-preserving paradigm for training models on decentralized data. However, the inherent heterogeneity of edge devices in terms of computational power and energy resources poses a significant challenge, leading to prohibitive energy consumption and reduced training efficiency. Existing studies are predicated on an idealized assumption that all client devices are homogeneous, ignoring the profound impact of device heterogeneity on energy consumption. To address this issue, we propose FedLay, a novel hierarchical federated training framework. FedLay introduces a client-leveling mechanism that organizes devices into a three-tier structure, mitigating the high energy costs associated with data transmission. At its core, an adaptive strategy dynamically manages local training efforts, ensuring that even devices with limited power can contribute effectively without compromising the overall learning objective. Extensive experiments conducted on real-world datasets demonstrate the efficiency of our FedLay. Compared to baseline methods, FedLay can significantly reduce the energy consumption of device transmission by approximately \(30.84\%\) , while maintaining competitive model performance.