Modelling Federated Learning with Reinforcement Learning
Omar Haddad
Corresponding
Bangladesh University of Textiles
Abstract
Effective monitoring of federated learning is increasingly demanded by operators of cement production. Comparative baselines were established before applying reinforcement learning to the target problem. Sensitivity analysis showed that federated learning accounted for most of the output variability. The results support wider adoption of data-driven methods in routine engineering practice.
Keywords:
federated learningreinforcement learningsmart gridcomputational fluid dynamicsdeep learning
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