| Abstract |
In the scope of smart cities, the sensors scattered throughout the city generate information that supplies intelligence mechanisms to learn the city's mobility patterns. These patterns are used in machine learning (ML) applications, such as traffic estimation, that allow for improvement in the quality of experience in the city. Owing to the Internet-of-Things (IoT) evolution, the city's monitoring points are always growing, and the transmission of the mass of data generated from edge devices to the cloud, required by centralized ML solutions, brings great challenges in terms of communication, thus negatively impacting the response time and, consequently, compromising the reaction in improving the flow of vehicles. In addition, when moving between the edge and the cloud, data are exposed, compromising privacy. Federated learning (FL) has emerged as an option for these challenges: (1) It has lower latency and communication overhead when performing most of the processing on the edge devices; (2) it improves privacy, as data do not travel over the network; and (3) it facilitates the handling of heterogeneous data sources and expands scalability. To assess how FL can effectively contribute to smart city scenarios, we present an FL framework, for which we built a testbed that integrated the components of the city infrastructure, where edge devices such as NVIDIA Jetson were connected to a cloud server. We deployed our lightweight container-based FL framework in this testbed, and we evaluated the performance of devices, the effectiveness of ML and aggregation algorithms, the impact on the communication between the edge and the server, and the consumption of resources. To carry out the evaluation, we opted for a scenario in which we estimated vehicle mobility inside and outside the city, using real data collected by the Aveiro Tech City Living Lab communication and sensing infrastructure in the city of Aveiro, Portugal. |
