Privacy-preserving Federated Adversarial Domain Adaptation over Feature Groups for Interpretability

We present a novel privacy-preserving federated adversarial domain adaptation approach (
$\mathbf{PrADA}$
) to address an under-studied but practical cross-silo federated domain adaptation problem, in which the party of the target domain is insufficient in both samples and features. We handle the lack-of-feature issue by extending the feature space through vertical federated learning with a feature-rich party and tackle the sample-scarce issue by performing adversarial domain adaptation from the sample-rich source party to the target party. In this work, we focus on financial applications where interpretability is critical. However, existing adversarial domain adaptation methods typically apply a single feature extractor to learn low-interpretable feature representations with respect to the target task. To improve interpretability, we exploit domain expertise to categorize the feature space into multiple groups that each group holds tightly relevant features, and we learn a semantically meaningful high-order feature from each feature group. In addition, we apply a fine-grained domain adaptation to each feature group to improve transferability. We design a privacy-preserving vertical federated learning framework that enables performing the PrADA securely and efficiently. We evaluate our approach based on two tabular datasets. Experiments demonstrate both the effectiveness and practicality of our approach.