D2BFT: A dual Byzantine fault tolerance approach for multi-agent drone surveillance with deep reinforcement learning

Abstract

The Dual Byzantine Fault Tolerance (D2BFT) framework integrates Practical Byzantine Fault Tolerance (PBFT) and Delegated Byzantine Fault Tolerance (DBFT) within a Multi-Agent Reinforcement Learning Proximal Policy Optimization (MARLPPO) environment to secure autonomous drone swarms for urban surveillance. Under a two-phase protocol, a subset of delegated validators conducts a fast PBFT-style consensus that a secondary group of validators then verifies. Deployed on Unity, D2BFT resists up to 40% malicious agents and provides a 20% improvement in the average consensus latency (0.60 s compared to 0.75 s for PBFT) with throughput of more than 30 transactions per second. Through realistic simulation of fault injections, Invert, Randomize, Silent, and Conflicting D2BFT ensure strong agreement with negligible overhead in the presence of high mobility and spotty connectivity. These findings validate the capacity of D2BFT to maintain resilience and efficiency, providing a scalable approach to fault-ridden real-time drone networks.