TY  - EJOU
AU  - Alfaras, Mohammed Shukur 
AU  - Karan, Oguz 
AU  - Kurnaz, Sefer 
AU  - Turkben, Ayca Kurnaz 

TI  - Fed-HOER: Federated Hybrid-Optimized Emotion Recognition Framework Using DBO-FLA Metaheuristic Optimization
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

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

DO  - 10.32604/cmc.2026.079577