Achieving Model Fairness in Vertical Federated Learning

Understand

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.