Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.079577
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Fed-HOER: Federated Hybrid-Optimized Emotion Recognition Framework Using DBO-FLA Metaheuristic Optimization

Mohammed Shukur Alfaras1,2,*, Oguz Karan3, Sefer Kurnaz1, Ayca Kurnaz Turkben4
1 Department of Electrical and Computer Engineering, Engineering College, Altinbas University, Istanbul, Turkey
2 Information and Communications, Planning Department, Babil Education Directorate, Ministry of Education, Hillah, Babil, Iraq
3 Department of Research and Development, Siemens A.S, Istanbul, Turkey
4 Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Rumeli University, Istanbul, Turkey
* Corresponding Author: Mohammed Shukur Alfaras. Email: email, email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.079577

Received 23 January 2026; Accepted 11 May 2026; Published online 27 May 2026

Abstract

Despite deep learning’s high precision in emotion identification, centralized training is associated with privacy and scalability concerns. The privacy-preserving federated learning model, Federated Hybrid-Optimized Emotion Recognition (Fed-HOER), introduced in this paper is an auto-tuning hyperparameters optimizer based on a hybrid Dung Beetle Optimizer-Fick’s Law Algorithm (DBO-FLA) optimizer. The global and local searches are optimized at two levels, and validation loss is minimized by 22%–24% without sharing raw data. The experiments on Extended Cohn–Kanade (CK+), Japanese Female Facial Expressions (JAFFE), and Karolinska Directed Emotional Faces (KDEF) exhibit a high generalization rate with a mean accuracy of 98.14. The findings demonstrate that Fed-HOER is statistically significantly better than baseline configurations. The results show that the suggested framework offers a favorable trade-off between predictive accuracy and privacy protection, which is why it can be used in the healthcare, educational, and other emotion-related fields.

Keywords

Emotion recognition; affective computing; federated learning; Dung Beetle Optimizer (DBO); Fick’s Law Algorithm (FLA); hybrid metaheuristic optimization; privacy preservation; convolutional neural networks (CNN)

  • 199

    View

  • 45

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link