Open Access

ARTICLE

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

Mohammed Shukur Alfaras^1,2,*, 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 Authors: Mohammed Shukur Alfaras. Email: ,

Computers, Materials & Continua 2026, 88(2), 70 https://doi.org/10.32604/cmc.2026.079577

Received 23 January 2026; Accepted 11 May 2026; Issue published 15 June 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.

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Keywords

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