Privacy-Preserving Smart Agriculture System Using Federated Learning

A privacy-preserving smart agriculture system that uses federated learning.

A privacy-preserving smart agriculture system that uses federated learning. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to connect hardware to software: reading sensors, sending data to a back end, visualising it and acting on it with alerts or control.

The main parts are privacy-preserving training across several clients, with only model updates shared, security and privacy controls such as encryption and access control, sensor or device data collection, a messaging layer between devices and the server, and a live dashboard. The reference build uses Flower, ESP32, MQTT, Python, and Node-RED, with a clear project structure, setup instructions and sample data so that it can be run and extended.