This letter studies a vertical federated edge learning (FEEL) system for
collaborative objects/human motion recognition by exploiting the distributed
integrated sensing and communication (ISAC). In this system, distributed edge
devices first send wireless signals to sense targeted objects/human, and then
exchange intermediate computed vectors (instead of raw sensing data) for
collaborative recognition while preserving data privacy. To boost the spectrum
and hardware utilization efficiency for FEEL, we exploit ISAC for both target
sensing and data exchange, by employing dedicated frequency-modulated
continuous-wave (FMCW) signals at each edge device. Under this setup, we
propose a vertical FEEL framework for realizing the recognition based on the
collected multi-view wireless sensing data. In this framework, each edge device
owns an individual local L-model to transform its sensing data into an
intermediate vector with relatively low dimensions, which is then transmitted
to a coordinating edge device for final output via a common downstream S-model.
By considering a human motion recognition task, experimental results show that
our vertical FEEL based approach achieves recognition accuracy up to 98\% with
an improvement up to 8\% compared to the benchmarks, including on-device
training and horizontal FEEL.

研究了一种基于分布式综合感知和通信（ISAC）的垂直联邦边缘学习（FEEL）系统，采用中间计算矢量（而非原始感知数据）实现目标物体 / 人运动识别，以保护数据隐私。通过实验表明，与基准方法（包括设备内训练和水平 FEEL）相比，垂直 FEEL 方法可实现高达 98% 的识别准确率，提高了 8%。