Application of federated learning in predicting breast cancer - Chai Jiarui (ITM Web of Conferences)
The prediction and diagnosis of breast cancer relies on multimodal data, such as imaging, genetic information, and patient lifestyle habits. Federated learning provides a framework to protect data privacy, allowing multiple institutions to share model training without sharing the original data. This paper proposes a breast cancer prediction model combined with federated learning, where each participant trains the model locally using multimodal data such as imaging, genes, and treatment history. During the local training process, the data is normalized and feature extracted, initially classified using support vector machines (SVM) or penalized logistic regression and optimized using stochastic gradient descent (SGD). Subsequently, each participant then sends the updated model parameters to the central server, where the FedAvg algorithm combines them to produce a global model. The model achieves data protection, also accurately predicts the progression and recurrence risk of breast cancer. Although federated learning effectively solves the privacy protection problem, the issues of data heterogeneity and model interpretability still need to be addressed. In the future, interpretability technologies (such as SHAP and LIME) and transfer learning can be combined to improve the transparency and adaptability of the model.