Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home. On the other hand, enormous data volumes with sufficient variation are needed for the deep learning models to be trained properly. This paper introduces a novel model for water consumption prediction in smart cities while preserving privacy regarding monthly consumption. The proposed approach leverages federated learning (FL) as a machine learning paradigm designed to train a machine learning model in a distributed manner while avoiding sharing the users data with a central training facility. In addition, this approach is promising to reduce the overhead utilization through decreasing the frequency of data transmission between the users and the central entity. Extensive simulation illustrate that the proposed approach shows an enhancement in predicting water consumption for different households.

翻译：水资源消耗仍是全球未来挑战中的重大议题。在以负载监控和需求响应为代表的应用中，深度学习模型通过利用智慧城市中海量消耗数据进行训练。一方面，所使用的信息具有隐私属性。例如，部署于用户居所内物联网架构中的智能水表所采集的精确数据，可能暴露家用电器详情乃至用户居家行为模式。另一方面，深度学习模型的充分训练需要具备足够多样性的海量数据。本文提出一种面向智慧城市水资源消耗预测的新型模型，能够在保障月度消耗数据隐私的前提下实现预测功能。该方法将联邦学习（FL）作为一种分布式机器学习范式加以应用，该范式在无需将用户数据传输至中央训练设施的条件下，实现机器学习模型的分布式训练。此外，该方法通过降低用户与中央实体间数据传输频率，有望减少系统开销。大量仿真实验表明，本方法在预测不同家庭水资源消耗方面表现出显著性能提升。