sensors Review Federated Learning in Smart City Sensing: Challenges and Opportunities Ji Chu Jiang 1 , Burak Kantarci 1, * , Sema Oktug 2 and Tolga Soyata 3 1 School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; jjian057@uottawa.ca 2 Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey; oktug@itu.edu.tr 3 Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; tolgasoyata@gmail.com * Correspondence: burak.kantarci@uottawa.ca; Tel.: +1-613-562-5800 (ext. 6955) Received: 1 September 2020; Accepted: 26 October 2020; Published: 31 October 2020   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. Keywords: federated learning; machine learning; smart cities sensing; internet of things; security; privacy 1. Introduction The global population is witnessing rapid annual growth, especially within urban city settings [1]. Maintaining efficient management of a wide span of information and resources is becoming increasingly more difficult amid growing population, electronic devices, and data transmission [2]. These challenges associated with the growth of such services has motivated governments to look for efficient ways to manage the operation of a city with respect to resource allocation and triggered initiatives around the world to have a connected city system where each component leverages the use of connected technology; these components include the following: economy and finance, citizens, governance, transportation (i.e., mobility), sustainability (i.e., environment), and smart living [3–6]. The latest advancements in wireless communication technology have propelled the widespread use of smart technologies, cloud computing, and the Internet of Things (IoT) [7]. IoT is the network of devices that enables connectivity between people, things or services [8–11]. Advances in manufacturing, sensor and cloud technologies results in a predicted up to 100 billion (with a minimum Sensors 2020, 20, 6230; doi:10.3390/s20216230 www.mdpi.com/journal/sensors