Federated Q-Learning-Based Optimization for Resource Allocation in Industrial IoT Networks

In Industrial Internet of Things (IIoT) contexts, efficient predictive maintenance and resource allocation is important to reducing downtime and improving operational performance. This study introduces a novel federated reinforcement learning approach that addresses these difficulties by enabling several agents to learn optimum maintenance policies jointly while maintaining data privacy. Using the Upper Confidence Bound (UCB) technique inside a Q-learning framework, this research dynamically balances exploration and exploitation to obtain the appropriate maintenance activities based on real-time equipment health and resource availability. Experimental findings show the potential for considerable increases in energy efficiency while reducing costs, demonstrating the algorithm’s usefulness in minimizing downtime and maximizing operational efficiency. The creation of a federated training system, as well as the use of UCB for predictive maintenance decision-making, were significant advances. 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. Furthermore, simulation results illustrate that the proposed method performs 9% better than other methods in terms of energy consumption and 12% in terms of mean episodic reward, demonstrating its efficiency in decision-making and data handling.