Balancing Privacy and Accuracy: Federated Learning with Differential Privacy for Medical Image Data
Muhammad Hamza Mehmood, Mahnoor Iqbal Khan, A. Z. M. Ibrahim · 2024
Federated Learning with differential privacy is increasingly employed across various domains to enhance data privacy. In the medical field, particularly with medical image data, it provides a crucial layer of protection for patient information. However, this approach often compromises detection performance. This study investigates the application of a federated setup combined with differential privacy to achieve a more favorable balance between accuracy and privacy. We utilized two datasets to evaluate the model's performance. On the Brain Tumor dataset, the model achieved an accuracy of 0.78, a precision of 0.70, and a recall of 0.60, with an epsilon of 5.6. For the OASIS dataset of Alzheimer disease, the model demonstrated a higher accuracy of 0.91, a precision of 0.93, and a recall of 0.91, with an epsilon of 3.825. These results indicate that our approach can effectively maintain privacy while ensuring substantial predictive performance.