Federated Learning Enhances Privacy for Breast Cancer Prediction

This study investigates the application of federated learning to develop robust predictive models for breast cancer tumor progression. The core objective is to leverage sensitive health data, including clinical, biomarker, demographic, and medical imaging information, without compromising patient privacy. The research specifically evaluates how federated learning addresses key deployment pillars: transparency, scalability, security, and fairness. The methodology involved comparing the performance of a federated learning framework against a centralized model, which was trained on aggregated data. The findings suggest that the federated approach can achieve predictive performance comparable to that of centralized models, while maintaining data locality and enhancing privacy. Furthermore, the paper examines strategies to improve secure model updates, ensure consistent performance across diverse patient subgroups, and support scalable deployment across multiple healthcare institutions. These results highlight the potential of privacy-preserving, multimodal predictive modeling for personalized cancer care, including future applications like digital twins for treatment planning.