Federated learning involves training statistical models over remote devices such as mobile phones while keeping data localized. Training in heterogeneous and potentially massive networks introduces opportunities for privacy-preserving data analysis and diversifying these models to become more inclusive of the population. Federated learning can be viewed as a unique opportunity to bring fairness and parity to many existing models by enabling model training to happen on a diverse set of participants and on data that is generated regularly and dynamically. In this paper, we discuss the current metrics and approaches that are available to measure and evaluate fairness in the context of spatial-temporal models. We propose how these metrics and approaches can be re-defined to address the challenges that are faced in the federated learning setting.
翻译:联邦学习涉及对诸如移动电话等远程设备进行统计模型培训,同时保持数据本地化; 各种网络和潜在大规模网络的培训为隐私保护数据分析提供了机会,并使这些模型多样化,以更加包容人口; 联邦学习可被视为一个独特的机会,通过使示范培训能够针对多种参与者和定期和动态生成的数据,使许多现有模型实现公平和平等; 我们在本文件中讨论现有的衡量尺度和方法,以衡量和评价空间时空模型中的公平性; 我们建议如何重新界定这些衡量尺度和方法,以应对联邦学习环境中面临的挑战。