Resilient Byzantine Agreement with Predictions

TL;DR

This paper introduces algorithms for Byzantine Agreement that leverage predictors to improve fault tolerance, establishing tight trade-offs between predictor accuracy and resilience in both authenticated and non-authenticated settings.

Contribution

It provides a complete characterization of the resilience-accuracy trade-offs and introduces algorithms that adapt to predictor correctness, with tight bounds and analysis of resilience degradation.

Findings

Algorithms tolerate up to α·n faulty nodes with correct predictors.

Resilience decreases linearly with the number of wrong predictions.

Authenticated setting improves robustness bounds compared to non-authenticated.

Abstract

This paper studies the Byzantine Agreement problem where the nodes have access to a predictor that flags nodes for suspicion of faulty (Byzantine) behavior. We focus on algorithmic resilience -- the maximum number of faulty nodes an algorithm can tolerate -- and present algorithms and impossibility results whose resilience depend on the accuracy of the predictor. As our first main result, we bring a complete characterization of the consistency--robustness trade-offs in both the non-authenticated and authenticated settings: for $n$ nodes and a parameter $\alpha \in [0, 1]$, we present algorithms that tolerate up to $\alpha \cdot n$ faulty nodes when the predictor is correct (consistency), and up to $\frac{1-\alpha}{2} \cdot n - 1$ faulty nodes when the predictor is arbitrarily wrong (robustness); in the authenticated setting the robustness bound improves to $(1-\alpha) \cdot n - 1$. These trade-offs are exactly tight as we show that one additional faulty node renders the problem impossible. Our second main result characterizes smoothness: the rate at which resilience degrades as the predictor becomes less accurate. We show that resilience linearly decreases in the number of wrong predictions as long as that number stays within a constant fraction of $n$. Concretely, in the non-authenticated setting each additional wrong prediction loses one unit of resilience, whereas in the authenticated setting the decline is halved since two wrong predictions are needed to lose one unit of resilience.