Privacy-Preserving Unsupervised Domain Adaptation in Federated Setting

Abstract

The training of deep neural networks relies on massive high-quality labeled data which is expensive in practice. To tackle this problem, domain adaptation is proposed to transfer knowledge from label-rich source domain to unlabeled target domain to learn a classifier that can well classify target data. However, people don't consider privacy issues in domain adaptation. In this paper, we introduce a novel method that builds an effective model without sharing sensitive data between source and target domain. Target domain party can benefit from label-rich source domain without revealing its privacy data. We transfer the traditional domain adaptation into a federated setting, where a global server contains a shared global model. Additionally, homomorphic encryption (HE) algorithm is used to guarantee the computing security. Experiments show that our method performs effectively without reducing the accuracy. Our method can achieve secure knowledge transfer and privacy-preserving domain adaptation.