Title: Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT Show Chinese title

Title: 计算和通信高效轻量级垂直联邦学习在智能建筑物联网中的应用

Abstract: With the increasing number and enhanced capabilities of IoT devices in smart buildings, these devices are evolving beyond basic data collection and control to actively participate in deep learning tasks. Federated Learning (FL), as a decentralized learning paradigm, is well-suited for such scenarios. However, the limited computational and communication resources of IoT devices present significant challenges. While existing research has extensively explored efficiency improvements in Horizontal FL, these techniques cannot be directly applied to Vertical FL due to fundamental differences in data partitioning and model structure. To address this gap, we propose a Lightweight Vertical Federated Learning (LVFL) framework that jointly optimizes computational and communication efficiency. Our approach introduces two distinct lightweighting strategies: one for reducing the complexity of the feature model to improve local computation, and another for compressing feature embeddings to reduce communication overhead. Furthermore, we derive a convergence bound for the proposed LVFL algorithm that explicitly incorporates both computation and communication lightweighting ratios. Experimental results on an image classification task demonstrate that LVFL effectively mitigates resource demands while maintaining competitive learning performance. Show Chinese abstract

Abstract: 随着物联网设备数量的增加和功能的增强，在智能建筑中，这些设备正超越基本的数据收集和控制，积极地参与到深度学习任务中。联邦学习（FL）作为一种去中心化的学习范式，非常适合此类场景。然而，物联网设备有限的计算和通信资源带来了显著挑战。尽管现有研究广泛探索了水平联邦学习（Horizontal FL）的效率改进，但由于数据划分和模型结构的根本差异，这些技术不能直接应用于垂直联邦学习（Vertical FL）。为了解决这一差距，我们提出了一种轻量级垂直联邦学习（LVFL）框架，该框架联合优化计算和通信效率。我们的方法引入了两种不同的轻量化策略：一种用于减少特征模型的复杂性以提高本地计算能力，另一种用于压缩特征嵌入以减少通信开销。此外，我们推导出了针对所提出的LVFL算法的收敛界限，该界限明确包含了计算和通信轻量化比率。在图像分类任务上的实验结果表明，LVFL有效地缓解了资源需求，同时保持了具有竞争力的学习性能。