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

TL;DR

This paper introduces PrADA, a privacy-preserving federated adversarial domain adaptation method that enhances interpretability by splitting feature spaces into meaningful groups, addressing sample scarcity and feature insufficiency in cross-silo financial applications.

Contribution

The work extends federated domain adaptation to improve interpretability through feature grouping and high-order feature learning, while ensuring privacy and efficiency.

Findings

Effective in improving domain adaptation performance

Enhances interpretability via feature grouping

Demonstrates practicality on real datasets

Abstract

We present a novel privacy-preserving federated adversarial domain adaptation approach ($\textbf{PrADA}$) 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 address 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 feature representations that are low-interpretable with respect to the target task. To improve interpretability, we exploit domain expertise to split the feature space into multiple groups that each holds relevant features, and we learn a semantically meaningful high-order feature from each feature group. In addition, we apply a feature extractor (along with a domain discriminator) for each feature group to enable a fine-grained domain adaptation. We design a secure protocol that enables performing the PrADA in a secure and efficient manner. We evaluate our approach on two tabular datasets. Experiments demonstrate both the effectiveness and practicality of our approach.