Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

TL;DR

This survey reviews the development of AI in cloud and edge computing, focusing on collaborative learning mechanisms, architectures, and emerging topics like pretrained models and reinforcement learning, highlighting current challenges and future directions.

Contribution

It is the first comprehensive review to systematically analyze cloud-edge collaborative AI architectures and mechanisms, including practical insights into advanced edge AI topics.

Findings

Established a systematic review of cloud-edge AI architectures

Identified key challenges in resource-constrained edge scenarios

Discussed promising directions like pretrained models and reinforcement learning

Abstract

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.