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

On-farm pest recognition requires models trained across growers who cannot share raw imagery, and federated distillation is the standard architectural answer. Its communication cost, however, is usually treated as an engineering overhead rather than as a quantity that governs what the aggregated student can be guaranteed to learn. the present analysis derives a communication-efficiency generalization bound for federated distillation in which the teacher ensemble's soft-label agreement, the number of communication rounds, and the on-farm data-residency constraint enter the bound explicitly. The derivation treats the distillation round as a contraction of the student-teacher disagreement in a reproducing-kernel Hilbert space, and the bound is stated as a function of the number of participating farms, the per-round communication budget, and the Rademacher complexity of the student hypothesis class. A design study characterises the operating characteristics of the bound under a declared variance structure with intraclass correlation 0.64, between-cluster standard deviation 6.0 and residual standard deviation 4.5, evaluated 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 predicts that 52 clusters are required for 80 per cent power at a 5 percentage-point effect, and the design study confirms that the bound's communication term dominates the generalization gap at small cluster counts. The contribution is a formal statement of when federated distillation inference is communication-efficient in a sense that matters for generalization, not merely for bandwidth.