A Resource-Efficient Cross-Domain Sensing Method for Device-Free Gesture Recognition With Federated Transfer Learning

Qi, Wanbin; Zhang, Ronghui; Zhou, Jiaen; Zhang, Hao; Xie, Yanxi; Jing, Xiaojun

doi:10.1109/tgcn.2022.3233825

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…Some DL-ISAC-oriented works cannot be grouped into one of the previous categories because they are rather focused on dispersed yet important applications. Gesture recognition: Qi et al [96] proposed a federated transfer learning framework for gesture recognition with Wi-Fi sensing as an indoor deployment for ISAC. Signal design: Xie et al [97] trained an autoencoder, in an unsupervised fashion, to extract the features of an input ISAC signal.…”

Section: Data-driven Methods For Other Isac Applicationsmentioning

confidence: 99%

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Salem,

Quamar,,

Magad

et al. 2023

Preprint

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<p>This paper aims to explore integrating various ML techniques into ISAC systems, covering various applications. These applications span intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs) and autonomous cars, as well as radar applications, localization and tracking, millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming. The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions. By synthesizing the existing knowledge and proposing new research avenues, this survey serves as a valuable resource for researchers, practitioners, and stakeholders involved in advancing the capabilities of ISAC systems in the context of 6G networks.</p>

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Section: Data-driven Methods For Other Isac Applicationsmentioning

confidence: 99%

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Salem,

Quamar,,

Magad

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…We did not find any work with the HBFL scheme using DNN. In edge-device learning network or resource-restricted systems [29][30][31], HBFL is widely investigated due to the natural fact that neither ID nor features sharing in a set of small data collectors. To be specific, mobile devices are usually power-limited or storage-limited, which leads to constrained computing & data transferring in user & client communication with confidentiality.…”

Section: Hybrid Federated Learning (Hbfl)mentioning

confidence: 99%

FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation

Meng,

Zhang,

Chen

et al. 2023

Preprint

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<div>Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with deep neural network (DNN) modeling is focused on horizontal data distributions, while vertical and hybrid schemes are much less studied. In this paper, we propose a generalized algorithm FedEmb, for modeling vertical and hybrid DNN-based learning. The idea of our algorithm is characterized by higher inference accuracy, stronger privacy-preserving properties, and lower client-server communication bandwidth demands as compared with existing work. The experimental results show that FedEmb is an effective method to tackle both split feature & subject space decentralized problems. To be specific, there are 0.3% to 4.2% improvement on inference accuracy and 88.9<em> % time </em>complexity reduction over baseline method.</div>

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Joint task offloading and resource allocation in mixed edge/cloud computing and blockchain empowered device-free sensing systems

Cheng

Li³

2023

Computer Communications

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A Resource-Efficient Cross-Domain Sensing Method for Device-Free Gesture Recognition With Federated Transfer Learning

Cited by 12 publications

References 23 publications

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation

Joint task offloading and resource allocation in mixed edge/cloud computing and blockchain empowered device-free sensing systems

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