An Efficient Resource Allocation Model in IIoT Using Federated Reinforcement Learning

Abstract

In the Industrial Internet of Things (IIoT), resource allocation is important for reducing downtime and improving the system's operational performance. This study introduces a novel federated reinforcement learning approach that addresses resource management difficulties by enabling several agents to learn optimum maintenance policies jointly while maintaining data privacy. According to the analysis and evaluation performed, the proposed technique has the potential for implementation in complex industrial contexts, with future work concentrating on integrating advanced predictive models and expanding the algorithm to include multi-objective optimization cases.