The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by partitioning the large model and offloading the primary training workload from edge devices to an edge server. However, the increasing number of participating devices and model complexity leads to significant communication overhead from the transmission of smashed data (e.g., activations and gradients), which constitutes a critical bottleneck for SL. To tackle this challenge, we propose SL-FAC, a communication-efficient SL framework comprising two key components: adaptive frequency decomposition (AFD) and frequency-based quantization compression (FQC). AFD first transforms the smashed data into the frequency domain and decomposes it into spectral components with distinct information. FQC then applies customized quantization bit widths to each component based on its spectral energy distribution. This collaborative approach enables SL-FAC to achieve significant communication reduction while strategically preserving the information most crucial for model convergence. Extensive experiments confirm the superior performance of SL-FAC for improving the training efficiency.

翻译：神经网络的日益复杂阻碍了在资源受限设备上部署分布式机器学习。分割学习（SL）通过划分大模型并将主要训练工作负载从边缘设备卸载到边缘服务器，提供了一种有前景的解决方案。然而，参与设备数量的增加和模型复杂度的提升，导致传输粉碎数据（如激活值和梯度）的通信开销显著增大，这构成了SL的关键瓶颈。为应对这一挑战，我们提出SL-FAC，一种通信高效的分割学习框架，包含两个关键组件：自适应频率分解（AFD）和基于频率的量化压缩（FQC）。AFD首先将粉碎数据变换到频域，并分解为具有不同信息的频谱分量；随后FQC根据各分量的谱能量分布，对其应用定制的量化位宽。这种协同方法使SL-FAC能够在策略性地保留对模型收敛最关键的深层信息的同时，实现显著的通信压缩。大量实验验证了SL-FAC在提升训练效率方面的优越性能。