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.

Italy suffers from a serious problem of water wastage, linked both to factors of education in the use of resources by citizens and to leaks in the pipelines due to obsolescence and wear and tear of the pipes, as well as the malfunctioning of the meters. The problems of the distribution network also determine inefficiencies (in particular interruptions in the water supply), which in the south of the country occur three times more frequently than in northern regions. Revelis, the company where I work, developed an IoT platform able to monitor a water delivery network in a district of Catanzaro (a small italian town that you'd probably didn't know before). The project is still under development but few milestones has been achieved. This component is responsible for the monitoring of several tracked objects.

Sensor Technology and Artificial Intelligence for Water Management seem quite fascinating. Likewise, water conservation for sustainable development has been and still the most apprehended process. A better conservation and management process would necessarily help the economy grow secure and stable for the future. From the elementary levels of education, we learn about the usage of water efficiently for better health and stability. Conservation and management have always been a moral trait and governing this with maximum advocacy was always the challenge.