2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems-Reference-Cited by-同舟云学术

2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems

Author:

Zhou Xiaokang¹^ORCID,Liang Wei²^ORCID,Ma Jianhua³,Yan Zheng⁴^ORCID,Wang Kevin I-Kai⁵^ORCID

Affiliation:

1. Faculty of Data Science, Shiga University, Hikone, Japan

2. School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha, China

3. Faculty of Computer & Information Sciences, Hosei University, Tokyo, Japan

4. State Key Laboratory on Integrated Services Networks and the School of Cyber Engineering, Xidian University, Xi'an, China

5. Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland, New Zealand

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Key R&D Program of Hunan Province of China

Open Fund of Key Laboratory of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering

Link

Reference40 articles.

2. UniMiB SHAR: A new dataset for human activity recognition using acceleration data from smartphones;micucci;Applied Sciences MDPI,2017

Cited by 64 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

2. HAR Data Analysis: Unveiling the Potential of Federated CNN Models;2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET);2024-05-16

3. GAN-Enhanced Vertical Federated Learning System for WHAR with non-IID Data;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06