AutoPilot: Learning to Steer High Speed Robust BFT

2026-06-08

AI Summary

Existing high-performance Byzantine Fault Tolerant (BFT) protocols suffer from performance degradation under dynamic workloads, heterogeneous networks, and adversarial behavior due to their reliance on static parameter configurations. This work proposes AutoPilot, the first framework to integrate decentralized reinforcement learning into BFT parameter tuning. By continuously monitoring runtime system states and dynamically optimizing consensus parameters, AutoPilot enables rapid adaptation to complex environments while maintaining robustness against adversarial perturbations. Implemented within Autobahn—a highly resilient BFT protocol—AutoPilot swiftly converges to optimal configurations in volatile settings, reducing end-to-end latency by 49.8% compared to default settings and outperforming random exploration by 73.3%.

Abstract

Recent Byzantine Fault Tolerant (BFT) protocols achieve strong performance by combining the low-latency advantages of leader-based BFT protocols with the high-throughput benefits of DAG-based data dissemination. Despite exposing a wide spectrum of internal tunable parameters, these protocols typically rely on static and heuristic configurations, which leads to performance degradation under dynamic workloads, heterogeneous network conditions, and evolving adversarial behaviors. In this paper, we present AutoPilot, a reinforcement learning-based framework that continuously monitors runtime conditions and dynamically adjusts protocol parameters online to optimize consensus performance. To ensure robustness, AutoPilot coordinates learning in a decentralized manner, providing resilience against adversarial data pollution. We implement AutoPilot on top of Autobahn, a state-of-the-art, highspeed, robust BFT protocol, and evaluate it across diverse dynamic environments. Experimental results demonstrate that AutoPilot quickly converges to the optimal configuration under changing environments, reduces end-to-end latency by 49.8% compared to the default protocol configuration, and outperforms random configuration exploration by 73.3%.

Problem

• Byzantine Fault Tolerance
• dynamic workloads
• heterogeneous networks
• adversarial behaviors
• protocol configuration

Innovation

• Reinforcement Learning
• Byzantine Fault Tolerance
• Dynamic Parameter Tuning
• Decentralized Optimization
• Consensus Protocol