Federated Learning Approach to Mitigate Water Wastage

TL;DR

This paper presents a federated learning system using low-cost hardware to optimize water usage in residential and agricultural settings, reducing water wastage while preserving user privacy and adapting to diverse environmental conditions.

Contribution

It introduces a novel federated learning approach with edge devices for water conservation, addressing privacy concerns and environmental variability.

Findings

Effective reduction in water wastage demonstrated

Preserved user privacy through local model training

Scalable solution adaptable to diverse environments

Abstract

Residential outdoor water use in North America accounts for nearly 9 billion gallons daily, with approximately 50\% of this water wasted due to over-watering, particularly in lawns and gardens. This inefficiency highlights the need for smart, data-driven irrigation systems. Traditional approaches to reducing water wastage have focused on centralized data collection and processing, but such methods can raise privacy concerns and may not account for the diverse environmental conditions across different regions. In this paper, we propose a federated learning-based approach to optimize water usage in residential and agricultural settings. By integrating moisture sensors and actuators with a distributed network of edge devices, our system allows each user to locally train a model on their specific environmental data while sharing only model updates with a central server. This preserves user privacy and enables the creation of a global model that can adapt to varying conditions. Our implementation leverages low-cost hardware, including an Arduino Uno microcontroller and soil moisture sensors, to demonstrate how federated learning can be applied to reduce water wastage while maintaining efficient crop production. The proposed system not only addresses the need for water conservation but also provides a scalable, privacy-preserving solution adaptable to diverse environments.