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.

翻译：不同人口群体之间的公平性是面对面任务的基本标准,《脸属性分类》是一个突出的例子。除了这一趋势外,联邦学习组织(FL)正日益获得牵引,成为分布式培训的可扩展范例。现有的FL方法要求数据同质性以确保公平性。然而,这一假设在现实世界环境中限制性太强。我们提议F3在数据差异性下为公平的FC提出一个新的FL框架。F3采用多种惯性来改善不同人口群体之间的公平性,而无需数据同质性假设。我们通过报告经验上观察到的公平性措施和大众脸数据集的准确性保障,来证明F3的效力。我们的结果表明F3在对FC的准确性和公平性实现实际平衡。