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<p>Deploying Federated Learning in multi-UAV networks faces dual challenges: severe resource heterogeneity and non-IID data distributions. Conventional FL frameworks (synchronous or asynchronous) typically address these issues in isolation, resulting in training inefficiencies and communication bottlenecks. To tackle these coupled challenges, we propose AhaFed, an Accelerated Hierarchical Aggregation framework. Specifically, AhaFed integrates: (1) a resource-aware periodic clustering protocol that dynamically groups UAVs to minimize intra-cluster disparities; (2) a hybrid synchronization scheme combining intra-cluster synchronous updates with inter-cluster asynchronous aggregation to mitigate straggler effects; and (3) a delay-aware attention mechanism that weighs updates based on parameter similarity and timeliness to counteract model staleness. Extensive experiments on benchmark datasets demonstrate that AhaFed outperforms baselines, accelerating convergence by 15%–95% and improving model accuracy by up to 40.63% in highly heterogeneous scenarios.</p>

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Ahafed: accelerated hierarchical aggregation for heterogeneous federated learning in multi-UAV networks

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Xin Liu,
  • Guanlin Wu,
  • Shengze Li,
  • Yu Liu,
  • Yuan Li,
  • Shiyuan Yu,
  • Xiong Li

摘要

Deploying Federated Learning in multi-UAV networks faces dual challenges: severe resource heterogeneity and non-IID data distributions. Conventional FL frameworks (synchronous or asynchronous) typically address these issues in isolation, resulting in training inefficiencies and communication bottlenecks. To tackle these coupled challenges, we propose AhaFed, an Accelerated Hierarchical Aggregation framework. Specifically, AhaFed integrates: (1) a resource-aware periodic clustering protocol that dynamically groups UAVs to minimize intra-cluster disparities; (2) a hybrid synchronization scheme combining intra-cluster synchronous updates with inter-cluster asynchronous aggregation to mitigate straggler effects; and (3) a delay-aware attention mechanism that weighs updates based on parameter similarity and timeliness to counteract model staleness. Extensive experiments on benchmark datasets demonstrate that AhaFed outperforms baselines, accelerating convergence by 15%–95% and improving model accuracy by up to 40.63% in highly heterogeneous scenarios.