"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning. (arXiv:2206.12183v1 [cs.LG])

Federated learning allows many devices to collaborate in the training of
machine learning models. As in traditional machine learning, there is a growing
concern that models trained with federated learning may exhibit disparate
performance for different demographic groups. Existing solutions to measure and
ensure equal model performance across groups require access to information
about group membership, but this access is not always available or desirable,
especially under the privacy aspirations of federated learning. We study the
feasibility of measuring such …

fix lg performance