Combining wireless sensing and edge intelligence, edge perception networks enable intelligent data collection and processing at the network edge. However, traditional sample partition based horizontal federated edge learning struggles to effectively fuse complementary multiview information from distributed devices. To address this limitation, we propose a vertical federated edge learning (VFEEL) framework tailored for feature-partitioned sensing data. In this paper, we consider an integrated sensing, communication, and computation-enabled edge perception network, where multiple edge devices utilize wireless signals to sense environmental information for updating their local models, and the edge server aggregates feature embeddings via over-the-air computation for global model training. First, we analyze the convergence behavior of the ISCC-enabled VFEEL in terms of the loss function degradation in the presence of wireless sensing noise and aggregation distortions during AirComp.

翻译：边缘感知网络通过融合无线感知与边缘智能，实现了在网络边缘进行智能数据采集与处理。然而，基于样本划分的传统水平联邦边缘学习难以有效融合来自分布式设备的互补多视角信息。为克服这一局限，本文提出一种专为特征划分感知数据设计的垂直联邦边缘学习框架。我们研究一个具备集成感知、通信与计算能力的边缘感知网络，其中多个边缘设备利用无线信号感知环境信息以更新本地模型，边缘服务器则通过空中计算聚合特征嵌入进行全局模型训练。首先，我们分析了在存在无线感知噪声及空中计算聚合失真的情况下，支持ISCC的VFEEL框架在损失函数衰减方面的收敛行为。