Abstract
Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalized cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumor progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness.
This study evaluates a federated learning framework using multimodal data, including clinical information, tumor characteristics, biomarker data, and patient demographics, alongside medical imaging data such as MRI scans, to model changes in tumor characteristics over time. The performance of the federated approach was compared with that of a centralized model trained on aggregated data.
The report then further examines strategies to enhance secure model updates, maintain performance across patient subgroups, and support scalability across institutions. The findings assess whether federated learning can achieve predictive performance comparable to centralized learning while preserving data locality. These results contribute to understanding the feasibility of privacy-preserving, multimodal predictive modeling and support future applications such as digital twins to assist clinicians and patients in personalized treatment planning.
Blogger's Review: This paper demonstrates the immense potential of federated learning in the medical field, particularly in enhancing predictive accuracy while safeguarding patient privacy. The integration of multimodal data offers new insights for personalized healthcare, warranting further exploration and implementation.