A Review on Suitability of Vertical Federated Learning in Smart City Platforms

Smart city model comprises various connected devices and IoT (Internet of Things) sensors under one umbrella of monitor, control and efficient decision making and distribution. Traffic control and monitoring, parking system and street lights, healthy and smart citizens, environment sustainability and development and many more are part of smart city and thriving for improvement and innovation. Data is collected through relevant heterogeneous data sources for processing and secure/ efficient controlling. However, privacy of heterogeneous raw data leads to study of various, privacy preserving, efficient and suitable techniques like, distributed computing, federated learning etc. Federated Learning techniques found to be promising one. This survey presents in-depth study on various Machine learning techniques suitable for smart city environment. Besides, study on (i) heterogeneous data of smart city platform (ii) suitable machine learning algorithms (iii) suitability of Vertical Federated Learning. Also suggest research works waiting to be undertaken in future.