A Systematic Review of Edge AI: Challenges and Innovations in Decentralized Computing

Abstract

Edge AI enables real-time data processing on devices, reducing latency and reliance on cloud infrastructure. This paper reviews the key innovations in edge AI, including model compression, federated learning, and neuromorphic computing. We analyze its applications in autonomous systems, healthcare monitoring, and IoT devices. Challenges such as energy efficiency, security, and model accuracy trade-offs are discussed. Future directions highlight advancements in edge AI hardware and self-learning models.