Resilient Byzantine Agreement with Predictions

2026-05-19

AI Summary

This work introduces a predictor into Byzantine fault-tolerant consensus to dynamically enhance fault tolerance: when predictions are accurate, the system tolerates up to αn faulty nodes; even under prediction errors, it guarantees robust fault tolerance of at least ((1−α)/2)·n−1 in the non-authenticated model or (1−α)·n−1 in the authenticated model. The paper presents the first complete characterization of the tight trade-off boundary between consistency and robustness in both models, revealing a linear and smooth degradation of fault tolerance with respect to prediction error. Through a predictor-assisted consensus protocol, rigorous theoretical analysis, and impossibility proofs, the study establishes that this boundary is strictly tight.

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 $α\in [0, 1]$, we present algorithms that tolerate up to $α\cdot n$ faulty nodes when the predictor is correct (consistency), and up to $\frac{1-α}{2} \cdot n - 1$ faulty nodes when the predictor is arbitrarily wrong (robustness); in the authenticated setting the robustness bound improves to $(1-α) \cdot n - 1$. 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.

Problem

• Byzantine Agreement
• resilience
• predictions
• consistency-robustness trade-off
• smoothness

Innovation

• Byzantine Agreement
• Predictor-based Resilience
• Consistency-Robustness Trade-off
• Smoothness
• Fault Tolerance