Authors

* External authors

Venue

Date

Share

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

Tao Qi*

Fangzhao Wu*

Chuhan Wu*

Lingjuan Lyu

Tong Xu*

Hao Liao*

Zhongliang Yang*

Yongfeng Huang*

Xing Xie*

* External authors

NeurIPS 2022

2022

Abstract

Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way. Since in real-world applications the data may contain bias on fairness-sensitive features (e.g., gender), VFL models may inherit bias from training data and become unfair for some user groups. However, existing fair ML methods usually rely on the centralized storage of fairness-sensitive features to achieve model fairness, which are usually inapplicable in federated scenarios. In this paper, we propose a fair vertical federated learning framework (FairVFL), which can improve the fairness of VFL models. The core idea of FairVFL is to learn unified and fair representations of samples based on the decentralized feature fields in a privacy-preserving way. Specifically, each platform with fairness-insensitive features first learns local data representations from local features. Then, these local representations are uploaded to a server and aggregated into a unified representation for the target task. In order to learn fair unified representations, we send them to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representations inherited from the biased data. Moreover, for protecting user privacy, we further propose a contrastive adversarial learning method to remove privacy information from the unified representations in server before sending them to the platforms keeping fairness-sensitive features. Experiments on two real-world datasets validate that our method can effectively improve model fairness with user privacy well-protected.

Related Publications

Finding a needle in a haystack: A Black-Box Approach to Invisible Watermark Detection

ECCV, 2024
Minzhou Pan*, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin*

In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non watermarked dataset as a ref…

PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR

ICML, 2024
Kartik Patwari, Chen-Nee Chuah*, Lingjuan Lyu, Vivek Sharma

Current image anonymization techniques, largely focus on localized pseudonymization, typically modify identifiable features like faces or full bodies and evaluate anonymity through metrics such as detection and re-identification rates. However, this approach often overlooks …

COALA: A Practical and Vision-Centric Federated Learning Platform

ICML, 2024
Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize as task, data, and model levels. At the task level, COALA extends support from simple classification to 15 computer vision tasks, in…

  • HOME
  • Publications
  • FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.