Hybrid Makes Better: Hybrid Differential Privacy Medical Image Classification Based on Federated Learning

Machine learning has the potential to revolutionize medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will compromise patient privacy. Federated learning is a promising technique that protects patient privacy and improves the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving federal learning scheme via hybrid differential privacy for medical image classification (FHDM). Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm combines Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 9.60% compared to previous differential privacy schemes with the same medical image dataset and privacy budget.