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Water consumption remains a major concern among the world's futurechallenges. For applications like load monitoring and demand response, deeplearning models are trained using enormous volumes of consumption data in smartcities. On the one hand, the information used is private. For instance, theprecise information gathered by a smart meter that is a part of the system'sIoT architecture at a consumer's residence may give details about theappliances and, consequently, the consumer's behavior at home. On the otherhand, enormous data volumes with sufficient variation are needed for the deeplearning models to be trained properly. This paper introduces a novel model forwater consumption prediction in smart cities while preserving privacy regardingmonthly consumption. The proposed approach leverages federated learning (FL) asa machine learning paradigm designed to train a machine learning model in adistributed manner while avoiding sharing the users data with a centraltraining facility. In addition, this approach is promising to reduce theoverhead utilization through decreasing the frequency of data transmissionbetween the users and the central entity. Extensive simulation illustrate thatthe proposed approach shows an enhancement in predicting water consumption fordifferent households.

Federated Learning for Water Consumption Forecasting in Smart Cities