Always Evolving: A Systematic Review on Challenges and Needs to Scale RL & FL on Industrial Embedded Systems

Paper i proceeding, 2026

Federated Learning (FL) and Reinforcement Learning (RL) show significant potential for industrial embedded systems, but their application is hindered by challenges like hardware constraints, data heterogeneity, and safety requirements, creating a research-practice gap. This systematic literature review synthesizes the state-of-the-art deployment of FL and RL on such systems, structuring findings across four challenge categories to identify research gaps. Our analysis of 61 studies reveals a dominance of simulation (66%), and FL (62%), with scarce hardware deployments (18%). The key barriers to industrial adoption are a lack of large-scale, real-world validation and unaddressed scalability challenges.