An Insight into Federated Learning Based Digital Twin Applications in Smart City

The Internet of Things (IoT) and new developments in Artificial Intelligence (AI) have enhanced technologies based on smart cities, such as smart transportation, smart healthcare, and smart environmental management (IoT). Artificial intelligence (AI) was used to build virtual Digital Twins (DT), which are exact duplicates of real objects. DT uses in the manufacturing and industrial sectors have been successful, but smart city applications are still in their infancy. The primary reasons for this delay are a lack of confidence and privacy concerns around the transmission of sensitive data. DT may be used in combination with Federated Learning (FL) to ensure dependability and privacy preservation. In order to facilitate governance in smart city applications and their adoption in circumstances where time is of the essence and lives are on the line, the primary goal of this study is to merge these two cutting-edge technologies. The use of technology, businesses now create better products, identify and swiftly fix physical issues, and realize value and advantages more quickly than in the past. Give a complete examination of the various DTs for smart cities using FL model applications. The study's findings are utilized to pinpoint a few important problems and suggest solutions for enhancing FL-DT integration in applications.