Abstract
Background: Breast cancer diagnosis increasingly relies on data-driven learning from heterogeneous medical imaging sources. However, centralized deep learning approaches face major limitations, including privacy risks, institutional data silos, and limited generalization across imaging modalities.
Methods: This study proposes a privacy-aware federated learning framework integrating a hybrid CNN–Vision Transformer architecture for breast cancer classification under simulated non-identically distributed (non-IID) conditions. Public datasets representing different imaging modalities, BreakHis (histopathology), INbreast and CBIS-DDSM (mammography), and BUSI (ultrasound) are treated as independent federated clients to emulate multi-institutional collaboration. Each client trains a local model, and only the model parameters are aggregated via Federated Averaging, thereby preserving data locality. The hybrid architecture combines a ResNet-18 convolutional branch for local feature extraction with a Vision Transformer branch for global contextual representation.
Results: Across ten federated communication rounds, the global model demonstrates stable convergence under heterogeneous client distributions. The final global validation performance reaches 72.43% accuracy, AUC 0.7475, and F1-score 0.718. Evaluation on the pooled test cohort achieves approximately 84% overall accuracy with a weighted F1-score of 0.82, while malignant recall approaches 96%, prioritizing clinically critical cancer detection. Qualitative explainability analysis indicates that the model focuses on diagnostically relevant tissue regions.
Conclusion: The results demonstrate the feasibility of hybrid CNN–Vision Transformer training in a federated setting for privacy-aware breast cancer classification across heterogeneous imaging domains.
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Copyright (c) 2026 Bandhan Panda, Bibek Kumar Patro, Siba Sundar Das, Santosh Kumar Kar