Applications of AI in Decentralized Computing Systems: Harnessing Artificial Intelligence for Enhanced Scalability, Efficiency, and Autonomous Decision-Making in Distributed Architectures

Abstract

This study explores the strategic applications of Artificial Intelligence (AI) in decentralized computing systems, which distribute workloads across multiple autonomous nodes to enhance fault tolerance, scalability, and resource utilization. It examines the evolution of AI from symbolic reasoning to advanced deep learning, underscoring its pivotal role in modern technology across various industries. The integration of AI in decentralized systems offers significant benefits, including improved security through AI-based threat detection and automated protocols, enhanced performance via optimized resource management and network traffic, and facilitated interoperability for seamless cross-platform integration. However, challenges such as system complexity, resource overhead, and security risks remain. The study aims to identify novel AI applications within decentralized architectures, analyze their benefits and challenges, and provide insights into the interplay between these technologies to drive innovation in fields like healthcare, finance, and transportation. This comprehensive analysis includes theoretical foundations, case studies, and key themes such as scalability, security, and ethical considerations, contributing to the development of robust, intelligent decentralized systems.