The exploration of computational and communication efficiency within Federated Learning (FL) has emerged as a prominent and crucial field of study. While most existing efforts to enhance these efficiencies have focused on Horizontal FL, the distinct processes and model structures of Vertical FL preclude the direct application of Horizontal FL-based techniques. In response, we introduce the concept of Lightweight Vertical Federated Learning (LVFL), targeting both computational and communication efficiencies. This approach involves separate lightweighting strategies for the feature model, to improve computational efficiency, and for feature embedding, to enhance communication efficiency. Moreover, we establish a convergence bound for our LVFL algorithm, which accounts for both communication and computational lightweighting ratios. Our evaluation of the algorithm on a image classification dataset reveals that LVFL significantly alleviates computational and communication demands while preserving robust learning performance. This work effectively addresses the gaps in communication and computational efficiency within Vertical FL.

翻译：联邦学习（FL）中计算与通信效率的探索已成为一个突出且关键的研究领域。虽然现有大多数提升这些效率的工作集中于横向联邦学习，但纵向联邦学习的独特流程与模型结构使得横向FL技术无法直接应用。为此，我们提出轻量化纵向联邦学习（LVFL）概念，旨在同时提升计算与通信效率。该方法分别针对特征模型与特征嵌入采用轻量化策略：前者提升计算效率，后者增强通信效率。此外，我们建立了LVFL算法的收敛界，该界同时考虑了通信与计算的轻量化比率。在图像分类数据集上的算法评估表明，LVFL在保持稳健学习性能的同时，显著降低了计算与通信需求。本工作有效弥补了纵向FL在通信与计算效率方面的空白。