Federated focal modulated UNet for cardiovascular image segmentation

Federated learning facilitates collaborative training of machine learning models on data distributed across multiple locations, effectively addressing the privacy concerns by eliminating the need for data centralization - a critical consideration in medical image analysis. In healthcare applications like cardiovascular segmentation, datasets from individual sites often feature annotations for specific heart regions, leading to partial overlaps. To address this challenge, we present a two-step partial annotation framework for federated learning, featuring a hybrid 3D multi-encoding UNet enhanced with focal modulation layers in the second stage. This architecture enables specialized subnetworks to act as experts, extracting features tailored to specific regions of interest based on each client's data. To further improve feature extraction and differentiation, we incorporate focal modulation blocks and apply regularization by introducing an auxiliary generic decoder during training. Comprehensive experiments on diverse cardiac MRI datasets demonstrate that our approach significantly outperforms centralized learning models.