Achieving Model Fairness in Vertical Federated Learning

Abstract

Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learning models without disclosing their private data and model parameters. Similar to other machine learning algorithms, VFL faces demands and challenges of fairness, i.e., the learned model may be unfairly discriminatory over some groups with sensitive attributes. To tackle this problem, we propose a fair VFL framework in this work. First, we systematically formulate the problem of training fair models in VFL, where the learning task is modelled as a constrained optimization problem. To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds. The messages that the server sends to passive parties are deliberately designed such that the information necessary for local updates is released without intruding on the privacy of data and sensitive attributes. We rigorously study the convergence of the algorithm when applied to general nonconvex-concave min-max problems. We prove that the algorithm finds a $\delta$-stationary point of the dual objective in $\mathcal{O}({\delta }^{-4})$ communication rounds under mild conditions. Finally, the extensive experiments on three benchmark datasets demonstrate the superior performance of our method in training fair models.

arXiv comment13 pages, 2 figures

References in corpus (10)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Equality of Opportunity in Supervised Learning
• Fairness in Machine Learning: A Survey
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• Agnostic Federated Learning
• VAFL: a Method of Vertical Asynchronous Federated Learning
• Empirical Risk Minimization under Fairness Constraints
• A Communication Efficient Collaborative Learning Framework for Distributed Features
• Feature-Distributed SVRG for High-Dimensional Linear Classification
• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating

Cited by in corpus (1)

• Vertical Federated Learning: Concepts, Advances and Challenges