# Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation Source: [Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation]() Collection index: [llms.txt]() Data updated: 2026-05-01T05:00:04.848Z Authors: Martina Pavan, Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh Published: 2026-04-29T06:12:09.000Z ## Abstract 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. The key contribution is a strategy for generating synthetic samples and distributing them across clients to improve coverage of both underrepresented pathologies and imaging devices. Experimental results demonstrate that our approach significantly enhances model performance and generalization across heterogeneous institutions, with minimal computational overhead at the client side. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Skin Lesion Classification on ISIC (test)](): Skin lesion classification; ISIC (test); Accuracy (CP): 93.4. ## Original sources and implementation [Original paper]() [PDF]()