| Title |
Federated Learning Differential Privacy Medical Image Classification Algorithm Based on Hybrid Reality Technology |
| Authors |
(Qun Luo) ; (Zhendong Liu) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.5.579 |
| Keywords |
Hybrid reality technology; Federated learning; Differential privacy; CNN |
| Abstract |
With the continuous growth of medical data, traditional learning methods have limitations in processing private data and model training. A federated learning differential privacy medical image classification algorithm based on hybrid reality technology was proposed to conduct medical image analysis in a distributed environment.
Firstly, the differential privacy medical image classification based on federated learning was studied. Then, the results of the differential privacy medical image classification model based on federated learning were analyzed. The results confirmed that as the learning rate increased, the accuracy of the research algorithm gradually increased. When the learning rate was 0.1 and the iterations were 10, the research algorithm reached the highest accuracy, which was 99.82%. In addition, the study also analyzed the impact of the number of clients on algorithm performance. In the range of 8 to 12 clients, this research algorithm demonstrated significant advantages compared to traditional image classification algorithms. When the number of clients was 8, its AUC value reached its highest, at 92.23%. In summary, the research algorithm has shown significant advantages in medical image classification tasks. |