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Byzantine Agreement with Predictions

Naama Ben-David, Muhammad Ayaz Dzulfikar, Faith Ellen, Seth Gilbert

TL;DR

The paper investigates Byzantine Agreement with predictions, introducing classification predictions and studying their impact on performance. It proves a fundamental Ω($n^2$) message lower bound even with perfect predictions, while showing that classification predictions can yield $O( ext{min}ig elax rac{B}{n}+1, fig)$ rounds in synchronous BA, with matching lower bounds, and provides authenticated and unauthenticated protocols leveraging graded consensus and implicit committees. The approach combines a high-level guess-and-double wrapper with conditional BA (classification-based), and uses graded consensus to ensure strong unanimity; cryptographic tools enable more efficient leader committees in the authenticated setting. The results reveal a trade-off: predictions can sharply improve time complexity but do not reduce worst-case message complexity, highlighting practical implications for security-monitoring-informed distributed systems and outlining directions for reducing communication costs and extending to broader prediction models.

Abstract

In this paper, we study the problem of \emph{Byzantine Agreement with predictions}. Along with a proposal, each process is also given a prediction, i.e., extra information which is not guaranteed to be true. For example, one might imagine that the prediction is produced by a network security monitoring service that looks for patterns of malicious behavior. Our goal is to design an algorithm that is more efficient when the predictions are accurate, degrades in performance as predictions decrease in accuracy, and still in the worst case performs as well as any algorithm without predictions even when the predictions are completely inaccurate. On the negative side, we show that Byzantine Agreement with predictions still requires $Ω(n^2)$ messages, even in executions where the predictions are completely accurate. On the positive side, we show that \emph{classification predictions} can help improve the time complexity. For (synchronous) Byzantine Agreement with classification predictions, we present new algorithms that leverage predictions to yield better time complexity, and we show that the time complexity achieved is optimal as a function of the prediction quality.

Byzantine Agreement with Predictions

TL;DR

The paper investigates Byzantine Agreement with predictions, introducing classification predictions and studying their impact on performance. It proves a fundamental Ω() message lower bound even with perfect predictions, while showing that classification predictions can yield rounds in synchronous BA, with matching lower bounds, and provides authenticated and unauthenticated protocols leveraging graded consensus and implicit committees. The approach combines a high-level guess-and-double wrapper with conditional BA (classification-based), and uses graded consensus to ensure strong unanimity; cryptographic tools enable more efficient leader committees in the authenticated setting. The results reveal a trade-off: predictions can sharply improve time complexity but do not reduce worst-case message complexity, highlighting practical implications for security-monitoring-informed distributed systems and outlining directions for reducing communication costs and extending to broader prediction models.

Abstract

In this paper, we study the problem of \emph{Byzantine Agreement with predictions}. Along with a proposal, each process is also given a prediction, i.e., extra information which is not guaranteed to be true. For example, one might imagine that the prediction is produced by a network security monitoring service that looks for patterns of malicious behavior. Our goal is to design an algorithm that is more efficient when the predictions are accurate, degrades in performance as predictions decrease in accuracy, and still in the worst case performs as well as any algorithm without predictions even when the predictions are completely inaccurate. On the negative side, we show that Byzantine Agreement with predictions still requires messages, even in executions where the predictions are completely accurate. On the positive side, we show that \emph{classification predictions} can help improve the time complexity. For (synchronous) Byzantine Agreement with classification predictions, we present new algorithms that leverage predictions to yield better time complexity, and we show that the time complexity achieved is optimal as a function of the prediction quality.
Paper Structure (24 sections, 56 theorems, 9 algorithms)

This paper contains 24 sections, 56 theorems, 9 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Model
  4. Technical Overview of the Algorithms
  5. Classification
  6. Adapting to the Quality of Classification
  7. Conditional Byzantine Agreement with Classification: Unauthenticated
  8. Conditional Byzantine Agreement with Classification: Authenticated
  9. High-Level Guess-and-Double Strategy
  10. Classification Predictions
  11. Unauthenticated Byzantine Agreement using Classification
  12. Graded Consensus with Core Set
  13. Conciliation with Core Set
  14. Byzantine Agreement Algorithm with Classification
  15. Authenticated Byzantine Agreement using Classification
  16. ...and 9 more sections

Key Result

Theorem 1

For every deterministic algorithm that solves Byzantine Agreement with predictions in a synchronous system with at most $t$ faulty processes, there is some execution in which the predictions are $100\%$ correct and at least $\Omega(n + t^2)$ messages are sent by correct processes.

Theorems & Definitions (105)

  • Theorem 1: Restated from \ref{['theorem:message_lower_bound']}
  • Theorem 2: Restated from \ref{['theorem:unauth_ba_predicitions']}
  • Theorem 3: Restated from \ref{['theorem:auth_ba_predicitions']}
  • Theorem 4: Restated from \ref{['theorem:rndLowerBound']}
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • ...and 95 more