Federated Fairness without Access to Sensitive Groups
Group fairness in federated learning when sensitive attributes cannot be observed: we protect worst-case subgroups via adversarially learned weightings, with guarantees and experiments on vision and tabular benchmarks.
Feb 22, 2024
Fair Federated Learning
Approaches for fairness and robustness in decentralized learning — with and without access to sensitive attributes — with formal guarantees, evaluated on computer vision and tabular data.
Oct 1, 2023
Minimax Demographic Group Fairness in Federated Learning
We formulate group fairness in federated learning as a minimax optimization over demographic group risks and provide algorithms with formal performance guarantees — protecting the worst-off group across heterogeneous clients.
Jun 20, 2022
Blind pareto fairness and subgroup robustness
A minimax framework that protects worst-case, unknown subgroups without demographic labels, trading off between Pareto efficiency and blind subgroup robustness.
Jun 10, 2021