Online deep learning’s role in conquering the challenges of streaming data: a survey

Abstract In an era defined by the relentless influx of data from diverse sources, the ability to harness and extract valuable insights from streaming data has become paramount. The rapidly evolving realm of online learning techniques is tailored specifically for the unique challenges posed by streaming data. As the digital world continues to generate vast torrents of real-time data, understanding and effectively utilizing online learning approaches are pivotal for staying ahead in various domains. One of the primary goals of online learning is to continuously update the model with the most recent data trends while maintaining and improving the accuracy of previous trends. Based on the various types of feedback, online learning tasks can be divided into three categories: learning with full feedback, learning with limited feedback, and learning without feedback. This survey aims to identify and analyze the key challenges associated with online learning with full feedback, including concept drift, catastrophic forgetting, skewed learning, and network adaptation, while the other existing reviews mainly focus on a single challenge or two without considering other scenarios. This article also discusses the application and ethical implications of online learning. The results of this survey provide valuable insights for researchers and instructional designers seeking to create effective online learning experiences that incorporate full feedback while addressing the associated challenges. In the end, some conclusions, remarks, and future directions for the research community are provided based on the findings of this review.