From Prediction to Decision: Dynamic Maintenance Scheduling in Industrial Edge Computing via Federated DRL
This paper proposes FDDM, a federated deep reinforcement learning scheme in which edge agents learn closed-loop maintenance policies locally and collaboratively improve them across distributed industrial edge sites without sharing raw operational data.