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Certified Uncertainty Propagation in One-Shot Federated Bayesian Models via Posterior Event Transport

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Probabilistic certification of Bayesian neural networks lower-bounds the posterior probability that a model satisfies a verifier-defined safety property. In one-shot federated Bayesian learning, however, the deployed model is obtained by aggregating parameters drawn from client-specific posterior distributions, so local certificates do not directly guarantee safety of the aggregated model. This paper develops a deployment-consistent certification framework by propagating local posterior events through the deployment aggregation rule, with an exact geometric characterization for Federated Avera

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