AutoPilot: Learning to Steer High Speed Robust BFT
2026-06-08 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors developed AutoPilot, a system that uses reinforcement learning to automatically adjust settings in Byzantine Fault Tolerant (BFT) protocols while they run. Unlike previous BFT methods that use fixed settings, AutoPilot changes the parameters in real-time to handle different network conditions and attacks better. They tested AutoPilot on a top BFT protocol called Autobahn and found it reduced delays almost by half and outperformed random tuning attempts. This helps keep the system running efficiently even when the environment changes.
Byzantine Fault ToleranceBFT protocolsreinforcement learningleader-based BFTDAG-based data disseminationprotocol parametersdecentralized learningconsensus performancedynamic workloadsnetwork heterogeneity
Authors
Liangrong Chen, Yue Zhang, Eric Zhou, Mohammad Javad Amiri, Ryan Marcus, Chenyuan Wu
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%.