In the realm of healthcare where decentralized facilities are prevalent, machine learning faces two major challenges concerning the protection of data and models. The data-level challenge concerns the data privacy leakage when centralizing data with sensitive personal information. While the model-level challenge arises from the heterogeneity of local models, which need to be collaboratively trained while ensuring their confidentiality to address intellectual property concerns. To tackle these challenges, we propose a new framework termed Abstention-Aware Federated Voting (AAFV) that can collaboratively and confidentially train heterogeneous local models while simultaneously protecting the data privacy. This is achieved by integrating a novel abstention-aware voting mechanism and a differential privacy mechanism onto local models' predictions. In particular, the proposed abstention-aware voting mechanism exploits a threshold-based abstention method to select high-confidence votes from heterogeneous local models, which not only enhances the learning utility but also protects model confidentiality. Furthermore, we implement AAFV on two practical prediction tasks of diabetes and in-hospital patient mortality. The experiments demonstrate the effectiveness and confidentiality of AAFV in testing accuracy and privacy protection.

翻译：在医疗机构分散化普遍存在的医疗领域，机器学习面临着数据与模型保护两大挑战。数据层面的挑战涉及集中处理包含敏感个人信息的数据时可能引发的隐私泄露问题，而模型层面的挑战则源于本地模型的异构性——这些模型需要在确保机密性以解决知识产权问题的前提下进行协同训练。为应对这些挑战，我们提出了一种名为"弃权感知联邦投票"（AAFV）的新框架，该框架能够在保护数据隐私的同时，以机密方式协同训练异构本地模型。这一目标通过将创新的弃权感知投票机制与差分隐私机制集成到本地模型预测中实现。具体而言，所提出的弃权感知投票机制采用基于阈值的弃权方法，从异构本地模型中筛选高置信度投票，这不仅提升了学习效用，同时保障了模型机密性。此外，我们在糖尿病和院内患者死亡率两项实际预测任务中实现了AAFV框架。实验结果表明，AAFV在测试准确率和隐私保护方面均展现出卓越的有效性与机密性。