Edge AI, Federated Learning, and IoT: Convergence and Survey

Abstract

The integration of Edge Artificial Intelligence (AI), Federated Learning (FL), and the Internet of Things (IoT) is fundamental to the development of next-generation intelligent computing paradigms. While these technologies are advancing rapidly, their convergence into a unified Edge-FL-IoT framework presents significant complexity due to fragmented architectures and diverse operational requirements of the system. The primary objective of this paper is to provide a comprehensive analysis of the convergence of Edge AI, Federated Learning (FL), and the Internet of Things (IoT). To achieve this, we present a multi-dimensional framework for classifying and evaluating existing Edge-FL-IoT systems according to architectural design, communication protocols, and privacy protection strategies. The analysis demonstrates that the integration of Edge AI and FL addresses key limitations of traditional centralized AI, particularly by alleviating bandwidth constraints and mitigating data ownership vulnerability. In addition, the study identifies and categorizes unresolved technical challenges within Edge-FL-IoT environments, including resource heterogeneity among edge nodes, non-independent and identically distributed (non-IID) data and emerging security threats. The paper concludes by outlining promising directions for future research and discussing major technological trends, thereby providing a comprehensive roadmap for the design of secure, efficient, and scalable decentralized intelligent system.