Privacy-Preserving Federated Learning for Artificial Intelligence Agriculture Disease Detection Under Heterogeneous Farm Data Silos: A Computer Differential-Privacy Utility Evaluation

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Federated learning allows agricultural disease-detection models to be trained across farms without centralising raw imagery, but the differential-privacy noise added to protect each farm's updates degrades the very accuracy that motivates the collaboration. the present analysis formalises that trade-off for heterogeneous farm data silos and evaluates it computationally. A federated averaging protocol with per-client Gaussian mechanism is specified, and a utility model is derived that separates the privacy budget, the number of participating silos and the heterogeneity of local label distributions into distinct terms. The model is then evaluated in a replication-based computational study: 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points, under a declared variance structure with between-cluster SD 6.0, residual SD 4.5 and intraclass correlation 0.64. The contribution is a design-theoretic account of when differential-privacy noise in federated agricultural learning remains compatible with disease-detection accuracy, and a specification of the cluster counts and privacy budgets under which that compatibility holds.

• Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture

  arXiv (Cornell University) 2026 · 0 citations

• Evaluation of Differentially-Private Federated Distillation for Computer Agriculture Crop-Yield Prediction Under Cross-Silo Heterogeneity: An Artificial Intelligence Privacy-Utility Bound for Cooperative Data Sharing

  Zenodo (CERN European Organization for Nuclear Research) 2026 · 0 citations

• Differentially-Private Federated Learning for Computer Agriculture Pest Inference Across Smallholder Smartphone Fleets Under Cross-Device Heterogeneity: An Artificial Intelligence Privacy-Utility Bound

  Zenodo (CERN European Organization for Nuclear Research) 2026 · 0 citations

• Federated Inference for Computer Agriculture Pest-Risk Prediction Under Cross-Farm Non-IID Data: An Artificial Intelligence Differential-Privacy Utility Bound

  Zenodo (CERN European Organization for Nuclear Research) 2026 · 0 citations

• Privacy-Budget Evaluation for Computer Agriculture Federated Pest-Outbreak Detection Under Cross-Farm Sample Heterogeneity: An Artificial Intelligence Differential-Privacy Utility Bound

  Zenodo (CERN European Organization for Nuclear Research) 2026 · 0 citations

• Federated Learning for Artificial Intelligence Agriculture Crop Disease Detection Under Heterogeneous Farm Data: A Computer Privacy-Utility Trade-off Evaluation

  Zenodo (CERN European Organization for Nuclear Research) 2026 · 0 citations

• Privacy-Preserving Vertical Federated Learning for Artificial Intelligence Agriculture Disease Detection Under Cross-Cooperative Data Custody: A Computer Leakage-Audit Evaluation