FAME: A privacy-preserving dual-stage deep learning framework for breast ultrasound imaging using federated transfer and synthetic learning
Abdul Raheem, Zhen Yang, Ala Saleh Alluhaidan, Malik Abdul Manan, Shahzad Ahmed, Fahad Sabah, Sadique Ahmad · Digital Health · 2025
Background Automated breast ultrasound analysis is hindered by limited annotated data, institutional heterogeneity, and strict privacy regulations. This study proposes FAME (Federated Attention-guided Multi-task Ensemble Network), a privacy-preserving and data-efficient framework for joint segmentation and classification of breast ultrasound images in decentralized clinical environments. Methods Federated Attention-guided Multi-task Ensemble Network integrates Federated Transfer Learning with class-specific synthetic data generation via Auxiliary Classifier Generative Adversarial Networks to enhance training under data scarcity. Segmentation is performed using a Multi Attention U-Net (MAU-Net), while classification employs a dual-stage ensemble of ResNet50V2, NASNetLarge, and MAU-Net, followed by a meta-classifier. Privacy is preserved through Differential Privacy with Gaussian noise injection and Secure Aggregation for interclient model update protection. The model was trained and validated on the Breast Ultrasound Image (BUSI) dataset (780 images: 80% training, 10% validation, 10% testing) and further evaluated on independent test sets from the Breast Ultrasound Classification (BUSC) (407 images) and UDIAT (163 images) datasets. Statistical significance was assessed using paired t -tests against baseline models, and 95% confidence intervals were reported for all metrics. Results On the BUSI test set, FAME achieved 98.70 ± 0.27% accuracy, 96.82 ± 0.53% F1-score, and 0.978 area under the curve (AUC). On UDIAT, it reached 98.14 ± 0.31% accuracy, 94.04 ± 0.75% F1-score, and 0.960 AUC, while on BUSC, it achieved 96.92 ± 0.27% accuracy, 90.32 ± 0.80% F1-score, and 0.950 AUC. For segmentation, Dice Scores were 89.72 ± 0.53% (BUSI), 93.09 ± 0.49% (BUSC), and 87.98 ± 0.57% (UDIAT), consistently surpassing state-of-the-art baselines. Synthetic augmentation improved performance on underrepresented malignant cases and enhanced generalization under non-IID client data distributions. Conclusion Federated Attention-guided Multi-task Ensemble offers a scalable, privacy-compliant, and high-performing solution for multi-institutional breast ultrasound analysis. By combining federated learning, synthetic augmentation, and attention mechanisms, it provides a strong foundation for secure, collaborative breast cancer diagnosis.