  Fairness across different demographic groups is an essential criterion for
face-related tasks, Face Attribute Classification (FAC) being a prominent
example. Apart from this trend, Federated Learning (FL) is increasingly gaining
traction as a scalable paradigm for distributed training. Existing FL
approaches require data homogeneity to ensure fairness. However, this
assumption is too restrictive in real-world settings. We propose F3, a novel FL
framework for fair FAC under data heterogeneity. F3 adopts multiple heuristics
to improve fairness across different demographic groups without requiring data
homogeneity assumption. We demonstrate the efficacy of F3 by reporting
empirically observed fairness measures and accuracy guarantees on popular face
datasets. Our results suggest that F3 strikes a practical balance between
accuracy and fairness for FAC.
