Continual Learning for Smart City: A Survey
Li Yang Zhipeng Luo Shiming Zhang Fei Teng Tianrui Li
With the digitization of modern cities, large data volumes and powerful
computational resources facilitate the rapid update of intelligent models
deployed in smart cities. Continual learning (CL) is a novel machine learning
paradigm that constantly updates models to adapt to changing environments,
where the learning tasks, data, and distributions can vary over time. Our
survey provides a comprehensive review of continual learning methods that are
widely used in smart city development. The content consists of three parts: 1)
Methodology-wise. We categorize a large number of basic CL methods and advanced
CL frameworks in combination with other learning paradigms including graph
learning, spatial-temporal learning, multi-modal learning, and federated
learning. 2) Application-wise. We present numerous CL applications covering
transportation, environment, public health, safety, networks, and associated
datasets related to urban computing. 3) Challenges. We discuss current problems
and challenges and envision several promising research directions. We believe
this survey can help relevant researchers quickly familiarize themselves with
the current state of continual learning research used in smart city development
and direct them to future research trends.