Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data

TL;DR

This paper introduces AAFV, a federated learning framework that enables privacy-preserving collaboration among heterogeneous healthcare models using abstention-aware voting and differential privacy, improving accuracy and confidentiality.

Contribution

The paper presents a novel abstention-aware voting mechanism combined with differential privacy for secure, heterogeneous federated learning in healthcare.

Findings

Effective in diabetes prediction tasks

Enhances privacy protection in federated models

Improves testing accuracy with confidentiality

Abstract

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.