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Resilient Byzantine Agreement with Predictions

NeurIPS 2026 · Learning theory and algorithms › Prediction and decision losses · approach: Algorithms with explicit predictors · 0 citations (OpenAlex)

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Characterizes consistency-robustness-smoothness trade-offs for prediction-augmented Byzantine agreement (global and local predictions, with and without authentication), giving matching algorithms and impossibility results for resilience.

Problem

Byzantine agreement with predicted honest sets: how many faulty nodes can be tolerated when predictions of honest nodes may be wrong, and how resilience degrades as prediction error grows.

Approach

Use a predicted set P of trusted nodes, pad it deterministically to a minimum size set L tuned by trust parameter alpha, then run Phase King (non-authenticated) or Dolev-Strong (authenticated) inside L; matching indistinguishability arguments show these trade-offs are optimal and independent of the predictor. Smoothness is characterized via an error function counting mispredictions.

Key results

Surprising findings

Limitations

Experimental setup

Metrics: resilience consistency robustness smoothness

Baselines: prediction-free Byzantine agreement (n>3t non-authenticated) prediction-free authenticated BA (n>2t)

Builds on: Phase King Dolev-Strong Ben-David et al. BA with predictions Boyar et al. smoothness

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