# Federated Learning for Water Consumption Forecasting in Smart Cities

arXiv: https://arxiv.org/abs/2301.13036 · HTML: https://mlsanity.com/paper/2301.13036/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Federated Edge Learning : Design Issues and Challenges](https://mlsanity.com/paper/2009.00081/) (2020)
- [Building Energy Load Forecasting using Deep Neural Networks](https://mlsanity.com/paper/1610.09460/) (2016)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning](https://mlsanity.com/paper/2104.03169/) (2021)
- [Resource Provisioning in Edge Computing for Latency Sensitive Applications](https://mlsanity.com/paper/2201.11837/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data](https://mlsanity.com/paper/2309.01275/) (2023)
- [Federated Learning Priorities Under the European Union Artificial Intelligence Act](https://mlsanity.com/paper/2402.05968/) (2024)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning](https://mlsanity.com/paper/2308.02614/) (2023)
- [Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems](https://mlsanity.com/paper/2401.12356/) (2024)
- [Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems](https://mlsanity.com/paper/2510.12727/) (2025)

## Abstract (arXiv)

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.