Enhancing Security for Smart Ehealthcare System based on Federated Learning and Homomorphic Encryption

Medical records are one of the most sensitive types of data, so when applying machine learning models, it is necessary to ensure data privacy. In recent years, machine learning and pre-trained models have been developing rapidly, and medical data security when using those models is gaining strong appeal with researchers. This study proposes a federated learning model integrated with homomorphic encryption to enhance security and privacy while training machine learning models on medical datasets. Additionally, we conducted experiments with federated learning models using different data distribution ratios to evaluate the robustness of this approach. The results show that the CNN, ResNet50, ResNet152, and DenseNet169 models integrated with federated learning on the LIDC-IDRI dataset have comparable accuracy to centralized machine learning. Moreover, the federated learning model integrated with Homomorphic Encryption on the ResNet50 model showed a 4% increase in training time and a 36% increase in model size compared to federated learning without encryption.