aiqbench analysis
This research demonstrates that federated learning can effectively balance predictive accuracy for breast cancer progression with strict data privacy requirements across multiple institutions. By integrating multimodal inputs like MRI scans and clinical biomarkers, the study validates a framework where model performance remains comparable to centralized training while preserving patient data locality. The findings offer a practical pathway toward scalable, secure digital twins that could enhance personalized treatment planning without compromising sensitive health information.
Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction
arXiv:2607.19532v1 Announce Type: cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
I.2; I.4; I.5; J.3
arXiv:2607.19532 [cs.LG]
(or arXiv:2607.19532v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.19532 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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