The Impact of Federated Learning on Urban Computing

Abstract

In an era defined by rapid urbanization and technological advancements, this article provides a comprehensive examination of the transformative influence of Federated Learning (FL) on Urban Computing (UC), addressing key advancements, challenges, and contributions to the existing literature. By integrating FL into urban environments, this study explores its potential to revolutionize data processing, enhance privacy, and optimize urban applications. We delineate the benefits and challenges of FL implementation, offering insights into its effectiveness in domains such as transportation, healthcare, and infrastructure. Additionally, we highlight persistent challenges including scalability, bias mitigation, and ethical considerations. By pointing towards promising future directions such as advancements in edge computing, ethical transparency, and continual learning models, we underscore opportunities to enhance further the positive impact of FL in shaping more adaptable urban environments.