Privacy-Preserving Federated Learning Framework for Sustainable Crop Yield Prediction Across Distributed Smallholder Farm Networks

Abstract

An accurate forecast of the crop yield is crucial for food security but smallholder farmers in developing countries face obstacles when trying to leverage AI-based agricultural decision support. Conventional centralised machine learning algorithms also require farmers to disclose sensitive farming data to other parties, which have raised concerns over privacy and hindered adoption. Further, individual farms may only have limited labeled data (<300 samples per season) for training predictive models. This paper describes the development of a Privacy-Preserving Federated Learning Framework for distributed farm networks to collaborate in model training without sharing any raw data. The framework combines federated averaging with DP mechanisms and edge-efficient neural architectures to reach 87.3% yield prediction accuracy, which is close to the centralized models (89.1%) while providing full data sovereignty empowering users. Major innovation: the framework achieves 82.6% accuracy with only 200 samples per farm, while local isolated models give only 61.4%. Buoyed by validation on 450 smallholder farms in three countries (India, Kenya, Nigeria) and over diverse crop settings (rice, maize, wheat), we show that the generalization of the approach across regions as well as robustness to non-IID data distributions. Deployment applicability: Enable 500+ million smallholder farmers worldwide, while saving communication overhead by 73% compared with standard federated learning via adaptive aggregation. And the framework tackles convergence in privacy preservation, minimal-data learning and sustainable agricultural AI–a needed step for an inclusive digital agriculture revolution.