Federated Learning in Smart City Sensing: Challenges and Opportunities.
Federated Learning in Smart City Sensing: Challenges and Opportunities.
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DOI：
10.3390/s20216230
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发表时间：
2020-10-31
期刊：
影响因子：
--
通讯作者：
Soyata T
中科院分区：
文献类型：
--
作者：
Jiang JC;Kantarci B;Oktug S;Soyata T
智慧城市感知是促进向智慧城市服务过渡的新兴范式。物联网(IoT)的出现以及具有计算和传感功能的移动设备的广泛使用推动了需要在社会规模上进行数据采集的应用。这些有价值的数据可以用来训练高级人工智能(AI)模型，这些模型服务于各种智能服务，造福社会的方方面面。尽管效率很高，但以集中式机器学习模型为后盾的传统数据采集模型存在安全和隐私问题，并导致智能城市服务的大规模传感和数据提供的参与度较低。为了克服这些挑战，联合学习是一个新的概念，可以作为数据收集过程中遇到的隐私和安全问题的解决方案。这篇调查文章概述了智能城市感知及其当前的挑战，随后介绍了联合学习在应对这些挑战方面的潜力。对联合学习的最新方法进行了全面的讨论，并深入讨论了联合学习在智能城市感知中的适用性；对该领域的公开问题、挑战和机遇提供了明确的见解，作为研究这一主题的研究人员的指导。
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.
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DOI：
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发表时间：
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期刊：
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