Federated Intelligence in Immersive Technologies: Systematic Review of Privacy, Agents and Metaverse

Abstract

The convergence of Multi-Agent Systems (MAS) and immersive technologies represents a rapidly evolving research domain with significant implications for intelligent virtual environments. This systematic review presents a comprehensive comparative analysis of approaches, frameworks, and applications that integrate MAS within Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) environments. Following PRISMA guidelines, we systematically reviewed 170 peer-reviewed articles published between 2020-2025, ultimately analyzing 25 high-quality studies that met our inclusion criteria. Our analysis reveals diverse architectural approaches including distributed multi-agent reinforcement learning frameworks, federated learning-enabled systems, and adaptive collaborative platforms. Key application domains identified include healthcare informatics, vehicular metaverses, educational training environments, and immersive collaboration systems. The study identifies critical challenges in real-time performance optimization, scalability with large agent populations, and maintaining quality of experience in distributed environments. Our findings contribute a novel classification framework for MAS-immersive technology integration, highlighting the shift towards privacy-preserving federated approaches and the emergence of metaverse applications. We present quantitative analysis of research trends and provide a roadmap for future developments in this intersection of artificial intelligence and immersive computing.