论文标题
联合学习的智能街道灯光监控应用：收益和未来挑战
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges
论文作者
论文摘要
通过自动化的学习，以改进的智能城市应用程序的自动化学习来加速和增强数据的城市。在物联网（IoT）生态系统的背景下，数据通信通常是昂贵，效率低下，不可扩展并且缺乏安全性。联合学习（FL）在提供隐私和沟通有效的机器学习（ML）框架方面起着关键作用。在本文中，我们评估了在智能城市街道灯光监控应用程序中FL的可行性。针对Lampposts操作的分类任务的集中式和（完全）个性化的机器学习技术的基准评估FL。在这种情况下，合并FL显示出对分类任务的绩效最小的降低，但沟通成本和保留隐私的巨大改善。这些结果增强了FL的生存能力和物联网应用的潜力。
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalised machine learning techniques for the classification task of the lampposts operation. Incorporating FL in such a scenario shows minimal performance reduction in terms of the classification task, but huge improvements in the communication cost and the privacy preserving. These outcomes strengthen FL's viability and potential for IoT applications.
