Archimedes Optimization-Driven CNN in Federated Settings: A Case Study on Thyroid Disease Diagnosis
13th International Symposium on Intelligent Manufacturing and Service Systems (IMSS'25), Düzce, Türkiye, 25 - 27 Eylül 2025, ss.43-51, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.5281/zenodo.17530699
- Basıldığı Şehir: Düzce
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.43-51
- İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet
Özet
Artificial Intelligence (AI) is playing an
increasingly vital role in the healthcare industry, driving the transformation
toward smart systems that enhance diagnostic accuracy, enable personalized
treatment, and support efficient clinical decision-making. In this context,
predicting thyroid disease is critical for long-term patient management and
improving survival outcomes. However, traditional machine learning approaches
face limitations due to data privacy concerns and the distributed nature of
healthcare data. In this study, we propose a federated learning (FL) framework
for the classification of thyroid disease using convolutional neural networks
(CNNs). To further improve model performance, we employ the Archimedes
Optimization Algorithm (AOA) to optimize CNN parameters, ensuring efficient
convergence and enhanced predictive accuracy. FL approach enables collaborative
training across multiple healthcare institutions without exposing sensitive
patient data, thus preserving privacy through decentralized learning. We
evaluate and compare several federated learning algorithms and client trainings
based on classification accuracy and f1-score. Experimental results demonstrate
that the AOA-optimized CNN within the FL framework outperforms client local
trainings. The accuracy rate of 90.68% obtained with the FedProx algorithm
shows that it achieves performance comparable to that of the centralized
learning approach.