Hybrid AI Frameworks for Edge Computing: Balancing Efficiency and Scalability

Abstract

The growing demand for real-time data processing and decision-making in Internet of Things (IoT) systems has intensified the need for advanced solutions at the network edge. Hybrid Artificial Intelligence (AI) frameworks, which combine different AI techniques such as machine learning (ML), deep learning (DL), and reinforcement learning (RL), are emerging as powerful tools to optimize edge computing environments. This paper explores how hybrid AI frameworks can enhance both efficiency and scalability in edge computing by distributing computational workloads, reducing latency, and conserving energy. Through a detailed analysis of system architectures and performance metrics, this study highlights the advantages of hybrid AI frameworks in balancing resource allocation and improving task execution speeds, particularly in bandwidth-constrained and resource-limited edge environments. Our findings demonstrate that hybrid AI models outperform traditional singular AI approaches by dynamically adapting to network conditions, thereby maximizing system scalability and operational efficiency. These insights provide a pathway for deploying intelligent, autonomous edge systems capable of meeting the growing computational demands of IoT networks.