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This study explores the application of federated learning to develop predictive models for breast cancer progression using multimodal data, including clinical and imaging information. By comparing the performance of federated models against centralized models, the research addresses critical factors such as transparency, security, and fairness in healthcare data usage. The findings indicate that federated learning can achieve predictive performance comparable to centralized approaches while ensuring data privacy and locality, paving the way for personalized cancer care solutions.
Federated learning can match centralized models in predicting breast cancer progression while safeguarding patient privacy.
Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour 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, tumour characteristics, biomarker data, and patient demographics, alongside medical imaging data such as MRI scans, to model changes in tumour characteristics over time. The performance of the federated approach was compared with that of a centralised 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 centralised learning while preserving data locality. These results contribute to understanding the feasibility of privacy-preserving, multimodal predictive modelling and support future applications such as digital twins to assist clinicians and patients in personalised treatment planning.
