Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.3K

A new clustered federated learning algorithm for heterogeneous data in high-precision wireless sensing.

Zongrui Tian1, Jiasheng Tian1

  • 1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.

Frontiers in Artificial Intelligence
|February 20, 2026
PubMed
Summary

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

11.1K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Performance evaluation and personalized electric field prediction of the deep H1 coil in the human brain based on simulation and machine learning.

Electromagnetic biology and medicine·2025
Same author

Novel non-resonant, low-frequency pulse-current circuit for energy-efficient, low-noise transcranial magnetic stimulation.

Frontiers in neuroscience·2025
Same author

A novel pulse-current waveform circuit for low-energy consumption and low-noise transcranial magnetic stimulation.

Frontiers in neuroscience·2025
Same author

Vector scattering from one-dimensional periodic perfectly conducting surface: transverse magnetic polarization.

Journal of the Optical Society of America. A, Optics, image science, and vision·2014

This study introduces a novel clustering algorithm using Kullback-Leibler (KL) divergence for federated learning with heterogeneous data in wireless sensing. The method enhances recognition accuracy by effectively clustering clients and personalizing models.

Area of Science:

  • Computer Science
  • Machine Learning
  • Wireless Communication

Background:

  • Federated learning (FL) faces challenges with heterogeneous data in wireless sensing.
  • Existing FL algorithms struggle to effectively handle data variability across devices.

Purpose of the Study:

  • To develop a clustering-based federated learning algorithm for wireless sensing environments.
  • To address data heterogeneity using Kullback-Leibler (KL) divergence for improved model personalization and accuracy.

Main Methods:

  • Applied Principal Component Analysis (PCA) for dimension reduction of high-dimensional heterogeneous data.
  • Calculated KL divergence distances between clients for clustering, incorporating an average distance for aggregated clients.
  • Conducted federated learning within clusters to generate personalized models using wireless datasets.
Keywords:
KL divergencedata heterogeneityfederated learning algorithmpersonalized federated learning algorithmwireless sensing

Related Experiment Videos

Last Updated: May 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.3K

Main Results:

  • Iterative reclustering and model updates were performed to optimize cluster numbers and recognition accuracy.
  • The proposed KL divergence-based algorithm demonstrated superior recognition accuracy compared to existing methods.
  • Personalized models were successfully obtained for clients within identified clusters.

Conclusions:

  • The clustering-based federated learning approach effectively handles heterogeneous data in wireless sensing.
  • KL divergence is a viable metric for client clustering in federated learning, leading to enhanced performance.
  • The proposed algorithm offers a promising solution for improving personalized model accuracy in distributed learning environments.