Local Cloud-Based Collaborative Learning vs Other IIoT Decentralized AI Solutions: A Systematic Literature Review

Abstract The increasing complexity and dynamic nature of Industrial Internet of Things (IIoT) demand scalable, adaptive, intuitive, and real-time automation frameworks. This paper presents a systematic literature review (SLR) of edge- and cloud-based collaborative learning frameworks for predictive maintenance and smart manufacturing tasks. In this SLR, we highlight the under-utilization of distributed computational architectures that provide complete automation support (design and run-time), flexibility, scalability, and inter- & intra-cloud service exchange while adhering to security management and integrity principles for solving IIoT tasks using modern artificial intelligence (AI) models at the edge/cloud. Recently, many IIoT applications have been designed using AI models that require robust, low-latency, and data-secure frameworks. This demand drives a trend toward distributed computational architectures in which data storage and processing are partially or fully decentralized. Common paradigms addressing this resource distribution include edge computing, federated learning, and private or hybrid clouds. We analyze 50 recent studies against IoT characteristics, AI performance metrics, and network/system management requirements. Our findings reveal underutilization of distributed architectures that support automation, interoperability, and security. While most solutions rely on centralized or hybrid clouds, fewer than 5% adopt federated or transfer learning, and over 60% remain dependent on supervised models. We also introduce a comparative perspective on network and security management, showing that local/private cloud implementations can reduce control-plane overhead and synchronization latency, though gaps persist in dynamic bandwidth allocation and zero-trust adoption. Finally, we benchmark our previously proposed local cloud-based collaborative learning (CCL) model against state-of-the-art solutions, highlighting its strengths in automation and interoperability, as well as limitations in adaptive computation and intelligent offloading. This review identifies the research gaps and opportunities for integrating collaborative AI, secure automation, and hybrid architectures to meet Industry 5.0 objectives of resilience, sustainability, and human-centricity.