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The prevalence of the Internet of things (IoT) and smart meters devices insmart grids is providing key support for measuring and analyzing the powerconsumption patterns. This approach enables end-user to play the role ofprosumers in the market and subsequently contributes to diminish the carbonfootprint and the burden on utility grids. The coordination of tradingsurpluses of energy that is generated by house renewable energy resources(RERs) and the supply of shortages by external networks (main grid) is anecessity. This paper proposes a hierarchical architecture to manage energy inmultiple smart buildings leveraging federated deep reinforcement learning(FDRL) with dynamic load in a distributed manner. Within the context of thedeveloped FDRL-based framework, each agent that is hosted in local buildingenergy management systems (BEMS) trains a local deep reinforcement learning(DRL) model and shares its experience in the form of model hyperparameters tothe federation layer in the energy management system (EMS). Simulation studiesare conducted using one EMS and up to twenty smart houses that are equippedwith photovoltaic (PV) systems and batteries. This iterative training approachenables the proposed discretized soft actor-critic (SAC) agents to aggregatethe collected knowledge to expedite the overall learning procedure and reducecosts and CO2 emissions, while the federation approach can mitigate privacybreaches. The numerical results confirm the performance of the proposedframework under different daytime periods, loads, and temperatures.

A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources