Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Minrui Xu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shiwen, Mao, Zhu Han, Abbas Jamalipour, Dong In Kim, Xuemin Shen, Victor C. M. Leung,, and H. Vincent Poor

TL;DR
This survey explores the deployment, infrastructure, applications, and challenges of Artificial Intelligence-Generated Content (AIGC) services at mobile edge networks, emphasizing real-time personalization and privacy.
Contribution
It provides a comprehensive overview of mobile AIGC networks, including architecture, use cases, and security challenges, highlighting future research directions.
Findings
AIGC services enable real-time personalized content at mobile edges.
Collaborative cloud-edge-mobile infrastructure is essential for AIGC deployment.
Security and privacy are critical challenges in mobile AIGC networks.
Abstract
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for…
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Taxonomy
TopicsAI in cancer detection · Privacy-Preserving Technologies in Data · FinTech, Crowdfunding, Digital Finance
