@Article{cmc.2026.079577,
AUTHOR = {Mohammed Shukur Alfaras, Oguz Karan, Sefer Kurnaz, Ayca Kurnaz Turkben},
TITLE = {Fed-HOER: Federated Hybrid-Optimized Emotion Recognition Framework Using DBO-FLA Metaheuristic Optimization},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26991},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2026.079577}
}