School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey.
Sensors (Basel). 2020 Oct 31;20(21):6230. doi: 10.3390/s20216230.
Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.
智慧城市感知是一种新兴的范例，可以促进智慧城市服务的转变。物联网 (IoT) 的出现以及具有计算和感知功能的移动设备的广泛使用，推动了需要在社会范围内进行数据采集的应用程序的发展。这些有价值的数据可以被利用来训练先进的人工智能 (AI) 模型，为各种智慧城市服务提供服务，从而使社会受益。尽管它们很有效，但基于集中式机器学习模型的传统数据采集模型存在安全和隐私问题，导致在大规模感知和数据提供方面参与度较低，以支持智慧城市服务。为了克服这些挑战，联邦学习是一个新概念，可以作为解决数据收集过程中遇到的隐私和安全问题的一种解决方案。本文综述了智慧城市感知及其当前挑战，接着探讨了联邦学习在解决这些挑战方面的潜力。本文提供了联邦学习的最新方法的全面讨论，并深入探讨了联邦学习在智慧城市感知中的适用性；为研究这一主题的研究人员提供了该领域的开放性问题、挑战和机遇的清晰见解。
