针对边缘设备异构性，自适应调整批量大小与模型分割以提升联邦学习效率。
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
- 根据收敛边界动态调节各设备的批量大小和模型分割点。
- 在多个数据集上验证，相比现有方法提升训练效率与收敛速度。
- 适合资源差异大的边缘计算场景，如智能终端协同训练。
分割式联邦学习（SFL）通过层间模型划分，使边缘设备也能参与机器学习。然而，由于边缘设备算力差异大，现有方法易受慢节点影响。本文首先推导出紧密的SFL收敛边界，量化了不同批量大小（BS）与模型分割（MS）对性能的影响。基于此，提出HASFL框架，能自适应控制各设备的批量大小与模型分割，平衡通信与计算延迟，提升训练收敛性。在多个数据集上的大量实验表明，HASFL显著优于现有先进方法。
原文摘要 · Abstract (English)
Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing SFL approaches suffer significantly from the straggler effect due to the heterogeneous capabilities of edge devices. To address the fundamental challenge, we propose adaptively controlling batch sizes (BSs) and model splitting (MS) for edge devices to overcome resource heterogeneity. We first derive a tight convergence bound of SFL that quantifies the impact of varied BSs and MS on learning performance. Based on the convergence bound, we propose HASFL, a heterogeneity-aware SFL framework capable of adaptively controlling BS and MS to balance communication-computing latency and training convergence in heterogeneous edge networks. Extensive experiments with various datasets validate the effectiveness of HASFL and demonstrate its superiority over state-of-the-art benchmarks.
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