Particle Swarm Optimized Federated Learning for Efficient Classification of Medical Images

Abstract

In recent years developments in deep learning and machine learning techniques have had a major impact on the healthcare sector especially in the early diagnosis of diseases. The MedMNIST dataset contains medical images used for various diseaes diagnosis, and this dataset is analyzed with deep learning methods, making it possible to make important medical decisions. However privacy and security concerns encountered during the processing of medical data restrict traditional data processing methods. Federated Learning ensures data security by allowing training to be done on local devices without data being collected on a central server. In this study a method that improves the medical image classification task is proposed by integrating the federated learning approach with the Particle Swarm Optimization(PSO) algorithm. The PSO algorithm is used to optimize model parameters contributing to increased classification accuracy. In the proposed method the Convolutional Neural Network(CNN) model is used for image classification and local training is performed by preserving the privacy of the data with the federated learning method. Experimental results demonstrate that the proposed method effectively addresses the challenges of federated learning by achieving accuracy levels comparable to centralized state-of-the-art approaches, despite being trained across distributed clients. Furthermore, the method enhances data privacy by ensuring that raw data remains local, mitigating the security risks inherent in conventional centralized training paradigms. The integration of the PSO algorithm with the federated learning method allows the model parameters to be optimized more efficiently, increasing accuracy and making the training process more efficient. This method provides a safe and effective solution for medical image classification and has great potential especially in cases where data privacy is critical. © 2025 IEEE.