Abstract

Regional soil databases contain valuable information for digital soil mapping and precision agriculture, but privacy concerns, data ownership issues, and institutional barriers often prevent effective data sharing and collaborative model development. This study presents a comprehensive evaluation of federated learning approaches for building robust soil property prediction models while preserving data privacy and institutional autonomy. We implemented and compared three federated learning architectures: Federated Averaging (FedAvg), Federated Proximal (FedProx), and a novel Soil-Specific Federated Learning (SSFL) algorithm across seven regional soil databases from different agricultural institutions in North America and Europe. The databases collectively contained 147,832 soil samples with measurements of organic carbon, pH, clay content, nitrogen, phosphorus, and potassium across diverse agro-ecological zones. Each institution maintained local control of their data while contributing to a global model through privacy-preserving aggregation mechanisms. The SSFL algorithm achieved superior performance with R² values of 0.87 for organic carbon, 0.84 for pH, 0.81 for clay content, and 0.79 for nitrogen compared to traditional centralized learning (R² = 0.83-0.89) and individual institutional models (R² = 0.62-0.78). Communication efficiency was improved by 67% through gradient compression and selective parameter sharing. Differential privacy mechanisms ensured individual sample privacy with ε = 1.2 privacy budget while maintaining model utility. Cross-institutional validation demonstrated robust transferability with performance degradation of only 3-8% when models trained on one region were applied to another. The federated approach enabled discovery of 23% more significant soil-environment relationships compared to individual institutional analyses. Economic analysis revealed 45% cost reduction in model development compared to centralized approaches requiring data migration. Security audits confirmed protection against membership inference attacks and model inversion attacks. The study demonstrates that federated learning enables collaborative soil science research while addressing privacy, legal, and institutional constraints that traditionally limit data sharing. This approach has transformative potential for advancing digital soil mapping, supporting global soil monitoring initiatives, and enabling evidence-based agricultural decision-making across institutional boundaries.