论文标题
空间应用程序联合学习的公平性
Fairness in Federated Learning for Spatial-Temporal Applications
论文作者
论文摘要
联合学习涉及在远程设备（例如手机）上进行培训统计模型，同时保持数据本地化。在异质和潜在的大规模网络中进行的培训引入了保护隐私数据分析的机会，并使这些模型多样化，以使人们更加包含人群。联合学习可以被视为一个独特的机会，可以通过使模型培训能够在各种参与者以及定期和动态生成的数据上进行，从而为许多现有模型带来公平和平等的机会。在本文中，我们讨论了当前的指标和方法，这些指标和方法可在时空模型的背景下衡量和评估公平性。我们建议如何重新定义这些指标和方法，以应对联合学习环境中面临的挑战。
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.
