| Title | Federated Learning in Smart City Sensing: Challenges and Opportunities |
|---|---|
| ID_Doc | 37402 |
| Authors | Jiang, JC; Kantarci, B; Oktug, S; Soyata, T |
| Title | Federated Learning in Smart City Sensing: Challenges and Opportunities |
| Year | 2020 |
| Published | Sensors, 20.0, 21 |
| Abstract | 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. |
| https://www.mdpi.com/1424-8220/20/21/6230/pdf?version=1604998232 |
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