Towards Federated Learning for HVAC Analytics
Yun‐Zhe Guo, Dan Wang, Arun Vishwanath, Xu Cheng, Qi Lecky Li · 2020
In recent years, many machine learning (ML) models have been developed for enhancing the performance of heating, ventilation and air conditioning (HVAC) systems. In all these studies, it is commonly assumed that building data is collected and stored at a central location, usually a cloud server, where the ML models are trained. Collecting data in a centralized location introduces privacy concerns since building data can reveal sensitive information such as the arrival and departure patterns of occupants. In this paper, we advocate federated learning (FL), a new distributed learning paradigm, where an overall ML model is trained without the need for exchanging raw data between the data source and the cloud.