F3: Fair and Federated Face Attribute Classification with Heterogeneous Data. (arXiv:2109.02351v3 [cs.LG] UPDATED)

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 …

arxiv classification data face lg