针对医疗联邦学习中的数据分布差异，提出动态加权优化模型公平性。
A New Perspective to Boost Performance Fairness for Medical Federated Learning
- 从特征偏移角度出发，动态感知各医院模型差异
- 按差异大小分配权重，重新聚合各层模型参数
- 在两个医学图像分割基准上实现更公平的性能表现
提升联邦学习（FL）的公平性有助于促进健康可持续的合作，尤其在医疗领域。然而，现有公平联邦学习方法忽略了医疗联邦学习中不同医院间数据集存在领域偏移这一特性。本文提出Fed-LWR，从特征偏移这一关键问题入手，改善医疗联邦学习的性能公平性。具体而言，通过估计本地与全局模型在各层特征表示上的差异，动态感知全局模型在所有医院间的偏差，并根据差异大小分配更高权重给差异较大的医院。该权重用于逐层重新聚合本地模型，以获得更公平的全局模型。我们在两个广泛使用的联邦医疗图像分割基准上评估了该方法，结果表明，相比多个前沿公平联邦学习方法，本方法在性能公平性方面均表现更优。
原文摘要 · Abstract (English)
Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the specific characteristics of medical FL applications, i.e., domain shift among the datasets from different hospitals. In this work, we propose Fed-LWR to improve performance fairness from the perspective of feature shift, a key issue influencing the performance of medical FL systems caused by domain shift. Specifically, we dynamically perceive the bias of the global model across all hospitals by estimating the layer-wise difference in feature representations between local and global models. To minimize global divergence, we assign higher weights to hospitals with larger differences. The estimated client weights help us to re-aggregate the local models per layer to obtain a fairer global model. We evaluate our method on two widely used federated medical image segmentation benchmarks. The results demonstrate that our method achieves better and fairer performance compared with several state-of-the-art fair FL methods.
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