FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. (arXiv:2206.03200v2 [cs.LG] UPDATED)

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 machine learning methods usually rely on the centralized
storage of fairness-sensitive features to achieve model fairness, which are
usually inapplicable in federated scenarios. In …

adversarial fair federated learning framework