Interpretable Federated Reinforcement Learning for Large-Scale Distributed HVAC Control

Heating, Ventilation, and Air Conditioning (HVAC) systems represent one of the largest sources of energy consumption in residential buildings, a demand expected to increase as climate control becomes more widespread in developing regions. In recent years, data-driven approaches have been increasingly applied to enhance building energy efficiency. Yet, many of these models operate as opaque black boxes, limiting user trust and understanding. To address this challenge, interpretability has emerged as a critical aspect of intelligent control systems. In this work, we propose an Interpretable Artificial Intelligence (IAI) framework that integrates Reinforcement Learning (RL) and Evolutionary Computation within a Federated Learning (FL) setting. This approach enables the evolution of transparent, Decision Tree (DT)-based control policies using distributed data from multiple buildings in diverse climatic regions, while preserving data privacy. We evaluate the proposed method in a simulation-based optimization environment using Sinergym, demonstrating its effectiveness in achieving a balanced trade-off between energy consumption, occupant comfort, and interpretability.