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

Abstract

We present a novel privacy-preserving federated adversarial domain adaptation approach ($\text{PrADA}$) 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.

Published in IEEE Transactions on Big Data

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Cited by in corpus (3)

• Vertical Federated Learning: Concepts, Advances and Challenges
• Federated Domain Generalization: A Survey
• FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction