  Federated Learning (FL) offers a privacy-preserving approach to train models
on decentralized data. Its potential in healthcare is significant, but
challenges arise due to cross-client variations in medical image data,
exacerbated by limited annotations. This paper introduces Cross-Client
Variations Adaptive Federated Learning (CCVA-FL) to address these issues.
CCVA-FL aims to minimize cross-client variations by transforming images into a
common feature space. It involves expert annotation of a subset of images from
each client, followed by the selection of a client with the least data
complexity as the target. Synthetic medical images are then generated using
Scalable Diffusion Models with Transformers (DiT) based on the target client's
annotated images. These synthetic images, capturing diversity and representing
the original data, are shared with other clients. Each client then translates
its local images into the target image space using image-to-image translation.
The translated images are subsequently used in a federated learning setting to
develop a server model. Our results demonstrate that CCVA-FL outperforms
Vanilla Federated Averaging by effectively addressing data distribution
differences across clients without compromising privacy.
