Collaborative Learning (CL) grows in relevance within Industry 4.0, as it facilitates knowledge exchange among networked participants and integrates learnings from models into local manufacturing processes. This allows organisations to efficiently harness information from distributed data, reduce costs, and accelerate innovation. Existing research focuses on conventional CL strategies, wherein model aggregation is performed on a central server. This centralised architecture, however, introduces inherent vulnerabilities, including a single point of failure and privacy risks. Recent advancements indicate a paradigm shift toward decentralised collaborative learning (DeCL) systems, yet no unified set of requirements has been proposed for supporting their development and implementation. This study addresses the aforementioned research gap by conducting a systematic literature review. Drawing on a synthesis and analysis of existing studies, this review identifies a comprehensive set of 23 system requirements, organised into six key categories, specifically focused on designing DeCL systems in manufacturing. Future research should align with Industry 5.0 principles by incorporating the human-centric design for ethical artificial intelligence and worker inclusion, as well as incorporating sustainable, resource-efficient technologies to develop environmentally responsible DeCL systems.

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