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%。

具有分布式集成感知和通信的竖直联邦边缘学习

Vertical Federated Edge Learning with Distributed Integrated Sensing and  Communication

Human motion recognition (HMR) based on wireless sensing is a low-cost
technique for scene understanding. Current HMR systems adopt support vector
machines (SVMs) and convolutional neural networks (CNNs) to classify radar
signals. However, whether a deeper learning model could improve the system
performance is currently not known. On the other hand, training a machine
learning model requires a large dataset, but data gathering from experiment is
cost-expensive and time-consuming. Although wireless channel models can be
adopted for dataset generation, current channel models are mostly designed for
communication rather than sensing. To address the above problems, this paper
proposes a deep spectrogram network (DSN) by leveraging the residual mapping
technique to enhance the HMR performance. Furthermore, a primitive based
autoregressive hybrid (PBAH) channel model is developed, which facilitates
efficient training and testing dataset generation for HMR in a virtual
environment. Experimental results demonstrate that the proposed PBAH channel
model matches the actual experimental data very well and the proposed DSN
achieves significantly smaller recognition error than that of CNN.

本文提出了一种深度光谱网络（DSN），通过利用残差映射技术来增强 HMR 性能，并开发出一种基于原始自回归混合（PBAH）通道模型，为虚拟环境中 HMR 的数据集生成提供了高效的训练和测试数据集，实验结果表明，所提出的 PBAH 通道模型与实际实验数据非常匹配，并且所提出的 DSN 的识别错误率显著小于 CNN。

使用深度频谱网络和基于原语的自回归混合信道模型的无线感知

Wireless Sensing With Deep Spectrogram Network and Primitive Based  Autoregressive Hybrid Channel Model

Human motion recognition is one of the most important branches of
human-centered research activities. In recent years, motion recognition based
on RGB-D data has attracted much attention. Along with the development in
artificial intelligence, deep learning techniques have gained remarkable
success in computer vision. In particular, convolutional neural networks (CNN)
have achieved great success for image-based tasks, and recurrent neural
networks (RNN) are renowned for sequence-based problems. Specifically, deep
learning methods based on the CNN and RNN architectures have been adopted for
motion recognition using RGB-D data. In this paper, a detailed overview of
recent advances in RGB-D-based motion recognition is presented. The reviewed
methods are broadly categorized into four groups, depending on the modality
adopted for recognition: RGB-based, depth-based, skeleton-based and
RGB+D-based. As a survey focused on the application of deep learning to
RGB-D-based motion recognition, we explicitly discuss the advantages and
limitations of existing techniques. Particularly, we highlighted the methods of
encoding spatial-temporal-structural information inherent in video sequence,
and discuss potential directions for future research.

本文综述了近年来在 RGB-D 基础上的人体运动识别中，采用深度学习方法中卷积神经网络和循环神经网络的应用，通过四类方法（基于 RGB、基于深度、基于骨骼、基于 RGB+D）来探讨现有技术的优缺点，特别是强调了应用于视频序列固有的时空结构信息的编码方法，以及未来的研究方向。