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.

翻译：联邦学习（FL）提供了一种在去中心化数据上训练模型的隐私保护方法。其在医疗健康领域潜力巨大，但由于医学影像数据存在跨客户端变异，且标注有限，挑战随之而来。本文提出跨客户端变异自适应联邦学习（CCVA-FL）以解决这些问题。CCVA-FL旨在通过将图像转换到公共特征空间来最小化跨客户端变异。该方法首先对来自每个客户端的一部分图像进行专家标注，然后选择一个数据复杂度最低的客户端作为目标。随后，基于目标客户端的标注图像，使用基于Transformer的可扩展扩散模型（DiT）生成合成医学图像。这些合成图像捕捉了多样性并代表了原始数据，被共享给其他客户端。接着，每个客户端使用图像到图像转换技术将其本地图像转换到目标图像空间。转换后的图像随后在联邦学习设置中用于开发服务器模型。我们的结果表明，CCVA-FL在不损害隐私的前提下，通过有效处理客户端间的数据分布差异，其性能优于朴素联邦平均算法。