Federated learning for oncology: Breast Cancer subtyping Case Study Using Gene Expression

Machine Learning has been subject to numerous healthcare applications, aiming to enhance early diagnosis and treatment strategies. Due to privacy regulations, restricting personal information sharing across institutions, the challenge of gathering diverse large-scale datasets using patient information is significant. Federated Learning (FL) provides an innovative solution by sharing models between remote learners and a central server instead of exchanging sensitive data.In this paper, we compare three Deep Learning models trained in FL for breast cancer subtyping, using gene expression. We innovate by studying models using either raw, ordered, or 2D-converted expression arrays. Three recent architectures are implemented depending on the input type, including a Transformer, a 1D CNN (NeXt-TDNN) and a 2D CNN (ConvNeXt) respectively. Each model’s performance is evaluated in regards to client count, data distribution, and aggregation strategy in order to measure the impact of both client configurations and server parameters.The results suggest that CNNs share comparable performance to Transformers despite carrying a lower parameter count. Transformers offer more consistent accuracy, while CNNs are more stable to train. In FL, aggregator choice and server learning rate both impact performance. Compared with a centralized setup, FL tends to yield a higher accuracy. This study highlights the impact of training setup on performance, convergence and stability in federated learning, showcasing the challenges and benefits in applying collaborative methods to clinical research.