Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation

ArXi:2604.26324v1 Announce Type: new Exploiting deep learning in medical imaging faces critical challenges, including strict privacy constraints, heterogeneous imaging devices with varying acquisition properties, and class imbalance due to the uneven prevalence of pathologies. In this work, we propose FedSSG, a novel Federated Learning framework that addresses domain shifts caused by diverse imaging devices while mitigating the under-representation of rare pathologies.