Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
This addresses a practical issue in financial applications requiring interpretability, but it is incremental as it builds on existing federated and adversarial methods with domain-specific modifications.
The paper tackles the problem of cross-silo federated domain adaptation where the target domain lacks both samples and features, by extending features through vertical federated learning and using adversarial adaptation, resulting in improved interpretability and effectiveness on tabular datasets.
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.