Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models. In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems. We first evaluate how to measure the contributions of institutions toward predictive performance and bias in cross-silo medical FL with a Shapley value approximation method. In a second step, we design different reward systems incentivizing contributions toward high predictive performance or low bias. We then propose a combined reward system that incentivizes contributions toward both. We evaluate our work using multiple medical chest X-ray datasets focusing on patient subgroups defined by patient sex and age. Our results show that we can successfully measure contributions toward bias, and an integrated reward system successfully incentivizes contributions toward a well-performing model with low bias. While the partitioning of scans only slightly influences the overall bias, institutions with data predominantly from one subgroup introduce a favorable bias for this subgroup. Our results indicate that reward systems, which focus on predictive performance only, can transfer model bias against patients to an institutional level. Our work helps researchers and practitioners design reward systems for FL with well-aligned incentives for trustworthy ML.

翻译：联邦学习(FL)在医疗健康领域的机器学习(ML)模型训练中受到研究人员和实践者的高度关注。确保这些模型的可信性至关重要。尤其是偏差——定义为模型在不同亚群间预测性能的差异——可能导致对特定亚群的不公平性，这是可信ML模型中不受欢迎的现象。本研究探讨了医疗FL中偏差发生的程度，以及如何通过奖励系统防止过度偏差。我们首先采用Shapley值近似方法，评估如何衡量跨孤岛医疗FL中各机构对预测性能和偏差的贡献。第二步，我们设计了旨在激励高预测性能或低偏差贡献的不同奖励系统，随后提出一种兼顾两者的复合奖励系统。我们利用多个医疗胸部X光数据集评估工作，重点关注按患者性别和年龄划分的亚群。结果显示，我们能够成功衡量对偏差的贡献，且集成奖励系统有效激励了向低偏差高性能模型的贡献。尽管扫描数据的分区对整体偏差影响甚微，但数据主要来自某一亚群的机构会对此亚群产生有利偏差。我们的研究结果表明，仅关注预测性能的奖励系统可能将针对患者的模型偏差转移至机构层面。本工作有助于研究人员和实践者设计奖励系统，使FL激励机制与可信ML目标高度对齐。