This paper introduces FedGIN, a federated learning framework that uses augmentation-driven generalization to enable robust medical image segmentation across different imaging modalities (CT and MRI) without sharing sensitive patient data. It demonstrates that spatial-domain augmentations, specifically Global Intensity Nonlinear (GIN), significantly improve segmentation accuracy in cross-modmodality federated settings.