A ResNet variant with group normalization, trained with federated averaging, gradient clipping, and secure aggregation, reaches about 97.8% accuracy on BloodMNIST under a claimed differential privacy budget.
Federated learning and differential privacy for medical image analysis
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Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
A ResNet variant with group normalization, trained with federated averaging, gradient clipping, and secure aggregation, reaches about 97.8% accuracy on BloodMNIST under a claimed differential privacy budget.