Continual Learning for Smart City: A Survey

TL;DR

This survey reviews continual learning methods and applications in smart city development, highlighting recent advances, challenges, and future research directions in urban computing contexts.

Contribution

It provides a comprehensive categorization of CL methods, explores diverse smart city applications, and discusses current challenges and future research directions.

Findings

Extensive categorization of CL methods and frameworks.

Application coverage across transportation, environment, health, and safety.

Identification of key challenges and promising future directions.

Abstract

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.