  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.
