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

arXiv: https://arxiv.org/abs/2111.10934 · HTML: https://mlsanity.com/paper/2111.10934/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 44 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [Federated Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.02054/) (2019)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [NBDT: Neural-Backed Decision Trees](https://mlsanity.com/paper/2004.00221/) (2020)
- [FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning](https://mlsanity.com/paper/2111.08211/) (2021)
- [Private Federated Learning with Domain Adaptation](https://mlsanity.com/paper/1912.06733/) (2019)
- [No Peek: A Survey of private distributed deep learning](https://mlsanity.com/paper/1812.03288/) (2018)
- [Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning](https://mlsanity.com/paper/2007.06849/) (2020)
- [Federated Deep Learning with Bayesian Privacy](https://mlsanity.com/paper/2109.13012/) (2021)

## Built on by (most cited first, 28 of 28)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Source-Free Unsupervised Domain Adaptation: A Survey](https://mlsanity.com/paper/2301.00265/) (2022)
- [FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models](https://mlsanity.com/paper/2310.10049/) (2023)
- [Trading Off Privacy, Utility and Efficiency in Federated Learning](https://mlsanity.com/paper/2209.00230/) (2022)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning](https://mlsanity.com/paper/2208.08934/) (2022)
- [Federated Learning without Full Labels: A Survey](https://mlsanity.com/paper/2303.14453/) (2023)
- [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://mlsanity.com/paper/2305.00312/) (2023)
- [A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning](https://mlsanity.com/paper/2209.03885/) (2022)
- [FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation](https://mlsanity.com/paper/2301.12623/) (2023)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration](https://mlsanity.com/paper/2304.11432/) (2023)
- [Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion](https://mlsanity.com/paper/2305.04288/) (2023)
- [A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning](https://mlsanity.com/paper/2305.18400/) (2023)
- [Vertical Federated Learning with Missing Features During Training and Inference](https://mlsanity.com/paper/2410.22564/) (2024)
- [Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review](https://mlsanity.com/paper/2411.05874/) (2024)
- [UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration](https://mlsanity.com/paper/2501.11388/) (2025)
- [Secure Vertical Federated Learning Under Unreliable Connectivity](https://mlsanity.com/paper/2305.16794/) (2023)
- [Efficient Vertical Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.11236/) (2023)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [A Framework for Verifiable and Auditable Federated Anomaly Detection](https://mlsanity.com/paper/2203.07802/) (2022)
- [Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images](https://mlsanity.com/paper/2311.12589/) (2023)
- [FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction](https://mlsanity.com/paper/2407.18472/) (2024)
- [Privacy Attack in Federated Learning is Not Easy: An Experimental Study](https://mlsanity.com/paper/2409.19301/) (2024)
- [Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios](https://mlsanity.com/paper/2505.11035/) (2025)

## Abstract (arXiv)

We present a novel privacy-preserving federated adversarial domain adaptation approach (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.