PriFedDD: Federated Discrete Diffusion for Medical Image Generation with a Utility-Privacy Trade-Off

Abstract

Generative modeling for medical imaging requires accurate representation of anatomical structures while limiting direct exposure of patient data in cross-institutional collaboration. Although continuous diffusion models have shown effectiveness in natural image generation, their Gaussian corruption process is less aligned with discrete medical segmentation masks and may introduce artifacts in federated settings. To address this limitation, we propose PriFedDD, a privacy-aware federated discrete diffusion framework for medical image generation. PriFedDD formulates diffusion in a discrete state space to better model categorical segmentation data and incorporates an Information-Dense Encoding strategy that folds local spatial patterns into compact multi-channel tensors, reducing federated memory consumption and training time while preserving structural information. Experiments on standard benchmarks and medical datasets, including BraTS and Covid-Xray, demonstrate that PriFedDD achieves improved FID and KID for discrete segmentation generation compared to continuous baselines. Furthermore, privacy analysis based on PSNR and SSIM confirms the discrete formulation balances sample utility with reconstruction risk, and downstream augmentation experiments show the generated samples effectively improve federated learning performance on medical tasks.