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Abstract

Transfer learning is a technique that is widely used in machine learning to transfer knowledge learned from one task to another. In recent years, transfer learning has gained significant attention in the context of smart cities due to its potential to enhance the efficiency and effectiveness of various urban applications. This review paper presents an in-depth analysis of the application of transfer learning in smart cities. The review provides an overview of transfer learning approaches such as domain adaptation, multi-task learning, and meta-learning, and their applications in various smart city domains including transportation, energy, and public safety. The paper also discusses the challenges and limitations of transfer learning in smart cities, such as data privacy and security concerns, domain shift, and scalability issues. The potential of transfer learning in addressing some of the key challenges facing smart cities is also highlighted, such as the integration of heterogeneous data sources and the need for personalized services. This paper provides a comprehensive review of transfer learning approaches in the context of smart cities and identifies future research directions in this area.