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

• URL: http://arxiv.org/abs/2111.10934v1
• Date: Mon, 22 Nov 2021 01:13:43 GMT
• Title: Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
• Authors: Yan Kang, Yang Liu, Yuezhou Wu, Guoqiang Ma, Qiang Yang
• Abstract summary: PrADA is a privacy-preserving adversarial domain adaptation approach. We exploit domain expertise to split the feature space into multiple groups that each holds relevant features. We learn a semantically meaningful high-order feature from each feature group.
• Score: 12.107058397549771
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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