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<p>Federated learning (FL) has become a widely adopted paradigm for privacy-preserving model training. However, traditional FL protocols heavily rely on data transmission between clients and servers over the wide-area network (WAN), which is often constrained and unreliable, leading to high communication costs and slow convergence. To address these issues, we propose a LAN-aware FL (LanFL) protocol that efficiently leverages the local-area network (LAN) capacity. By enabling frequent model aggregation within the same LAN, LanFL significantly reduces the need for global aggregation over the WAN, thereby speeding up the training process. However, due to the unique challenges presented by LAN environments, effectively utilizing LAN resources while maintaining the original performance of FL is not straightforward. To overcome this, LanFL incorporates several key techniques: LAN-aware hierarchical aggregation, intra-LAN device topology construction, and inter-LAN heterogeneous bandwidth coordination. We also provide theoretical analysis to derive the convergence bound. Extensive real-world experiments are conducted and the experimental results show that <Emphasis FontCategory="NonProportional">LanFL</Emphasis> can significantly accelerate FL training, save WAN traffic, and reduce monetary cost while preserving the model accuracy.</p>

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Communication-efficient cross-device federated learning via LAN–WAN orchestration

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Jianfang Li,
  • Liangkui Ding,
  • Junyi Zheng,
  • Qichao Tan,
  • Xu Bao,
  • Jinliang Yuan

摘要

Federated learning (FL) has become a widely adopted paradigm for privacy-preserving model training. However, traditional FL protocols heavily rely on data transmission between clients and servers over the wide-area network (WAN), which is often constrained and unreliable, leading to high communication costs and slow convergence. To address these issues, we propose a LAN-aware FL (LanFL) protocol that efficiently leverages the local-area network (LAN) capacity. By enabling frequent model aggregation within the same LAN, LanFL significantly reduces the need for global aggregation over the WAN, thereby speeding up the training process. However, due to the unique challenges presented by LAN environments, effectively utilizing LAN resources while maintaining the original performance of FL is not straightforward. To overcome this, LanFL incorporates several key techniques: LAN-aware hierarchical aggregation, intra-LAN device topology construction, and inter-LAN heterogeneous bandwidth coordination. We also provide theoretical analysis to derive the convergence bound. Extensive real-world experiments are conducted and the experimental results show that LanFL can significantly accelerate FL training, save WAN traffic, and reduce monetary cost while preserving the model accuracy.