  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.
