Towards Truly Adaptive Byzantine Fault-Tolerant Consensus

To acheive maximum performance, Byzantine fault-tolerant (BFT) systems must be manually tuned when hardware, network, or workload properties change. This paper presents our vision for a reinforcement learning (RL) based Byzantine fault-tolerant (BFT) system that adjusts effectively in real-time to changing fault scenarios and workloads. We identify several variables that can impact the performance of a BFT protocol, and show how these variables can serve as features in an RL engine in order to choose the context-dependent best-performing BFT protocol in real-time. We further outline a decentralized RL approach capable of tolerating adversarial data pollution, where nodes share local metering values and reach the same learning output by consensus.