Benchmarking Evaluation of Federated Distillation for Computer Agriculture Disease Classifiers Under Heterogeneous Edge-Device Label Noise: An Artificial Intelligence Privacy-Utility Bound for Cooperative Surveillance

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 distillation promises to train plant-disease classifiers across edge devices without centralising raw leaf imagery, yet its privacy-utility behaviour under heterogeneous label noise remains uncharacterised for cooperative agricultural surveillance. The analysis treats the before-after-control-impact estimator as the evaluation instrument and characterises its operating characteristics under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The bound separates a distillation-consistency term, a noise-heterogeneity term and a privacy-budget term, and predicts that utility loss grows with the dispersion of device-level noise rather than with its mean. The design analysis indicates that 56 clusters are required to detect a 5 percentage point effect at 80 percent power under the declared variance structure. The contribution is a benchmarkable privacy-utility bound that converts label-noise heterogeneity into a design constraint for cooperative surveillance, together with an evaluation protocol that makes the bound testable without observational data.

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

  Zenodo (CERN European Organization for Nuclear Research) 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

• Federated Distillation Inference for Computer Agriculture Pest-Recognition Models Under On-Farm Data Residency Constraints: An Artificial Intelligence Communication-Efficiency Generalization Bound

  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

• 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