Privacy-Preserving federated learning: An application for big data load forecast in buildings

Internet of Everything (IoE) is playing key role to enable smart energy management in buildings. A huge amount of load data are accumulated continuously to control the energy systems inside buildings. Big data analysis and prediction play increasingly vital roles in making the building energy systems smarter. Meanwhile, the big data forecasting technique represents the key element for an efficient energy con-trol mechanism in buildings. However, the load forecast models require a huge amount of consumption data with enough variety to achieve high accuracy. Moreover, the collected IoE data are privacy-sensitive. That is, the fine-grained load data collected at the level of buildings may reveal information about the occupant's behaviors. In this paper, by using Federating Learning (FL) as a decentralized machine learning technique, buildings occupants' information can efficiently participate in the decision-making without re-vealing privacy through a cloud/Edge computing framework. In the FL approach, the big data are widely distributed between buildings, where a shared learning model is trained locally on each of the buildings. Nonetheless, the FL approach does not provide provable privacy guarantees. Moreover, FL requires partici-pating buildings to regularly exchange huge updates resulting in huge bandwidth consumption. The paper presents a new privacy-preserving bandwidth-efficient FL that relies on Differential Privacy to theoreti-cally provide privacy. Furthermore, to ensure the fast convergence of the FL model, a POWER-SELECTION protocol is introduced to select between buildings to participate in the FL round. By numerical analysis, the proposed FL approach is demonstrated to improve the accuracy of load forecast while compromising provable privacy. (c) 2023 Published by Elsevier Ltd.