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AI Research
arXiv CS.AI·May 21·8 min read
Resilient Byzantine Agreement with Predictions
TL;DR
Byzantine Agreement is a distributed computing problem where nodes must reach consensus despite some being faulty or malicious. This paper introduces algorithms that use a predictor—a component that flags suspicious nodes—to improve how many faults a system can tolerate, with resilience bounds tied directly to predictor accuracy.
✦ Why It Matters
Engineers designing fault-tolerant distributed systems can now trade predictor accuracy for reduced redundancy requirements, lowering hardware costs in consensus protocols.
Key Takeaways
- Predictor-assisted Byzantine Agreement enables higher fault tolerance than traditional fixed-resilience algorithms.
- Resilience directly scales with predictor accuracy; quantify prediction quality to determine system fault tolerance.
- Impossibility results establish fundamental limits on resilience gains from prediction; use to validate algorithm design.
- Apply characterization results to tune consensus protocols for your network's prediction capabilities and fault assumptions.
Full Summary
Byzantine Agreement is a foundational distributed systems problem where nodes must reach consensus despite some nodes being faulty or adversarially controlled (Byzantine faults). Traditional solutions have fixed resilience limits—typically tolerating at most one-third faulty nodes.
This work augments Byzantine Agreement algorithms with access to a predictor that flags nodes suspected of Byzantine behavior, enabling adaptive resilience. The paper presents both constructive algorithms and impossibility results that characterize how predictor accuracy directly impacts the maximum number of faulty nodes an algorithm can tolerate.
By providing a complete characterization of consistency-resilience tradeoffs, the work demonstrates that even imperfect predictions meaningfully improve algorithmic resilience. Results show concrete bounds on fault tolerance as a function of predictor accuracy, enabling system designers to quantify the value of prediction quality.