**FedFarm** is a mobile-oriented **Federated Learning (FL)** system designed to classify cassava leaf diseases directly on Android devices. It represents a comprehensive FL solution tailored to this specific agricultural challenge. The system operates on standard Android smartphones, utilizing a frozen **MobileNetV2** backbone running via **PyTorch Mobile** as a feature extractor—eliminating the need for on-device training of the base network.
Instead, it collaboratively trains a lightweight linear classification head across the fleet of devices using the **Federated Averaging (FedAvg)** algorithm, coordinated by the **Flower FL** framework via **gRPC** transport. This architecture enables distributed model training over a network of devices in the field without centralizing raw image data, thereby preserving farmer privacy while continuously improving a shared disease detection model.