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：水的消耗仍然是世界未来挑战中的一个主要问题。对于诸如载荷监测和需求反应等应用,深层次学习模式在智能城市使用大量消费数据进行了培训。一方面,使用的信息是私人的。例如,作为系统在消费者住所的IOT结构的一部分的智能计量所收集的准确信息,可以详细介绍电器,进而说明消费者在家中的行为。另一方面,需要大量数据,并有足够的差异,才能对深层学习模型进行适当培训。本文介绍了智能城市水消费预测的新模式,同时保留每月消费的隐私。拟议方法利用联合学习作为一种机器学习模式,旨在以分散的方式培训机器学习模式,同时避免与中央培训设施分享用户数据。此外,这一方法还有望通过减少用户和中央实体之间的数据传输频率来减少间接费用的使用。广泛模拟表明,拟议方法显示不同家庭水的预测量有所提高。