A survey of machine-learning-integrated consensus mechanisms: towards intelligent, resilient, and self-optimizing blockchains

Abstract

As blockchain continues to saturate into a technological infrastructure for decentralized trust and cost-effective data processing, existing consensus mechanisms remain burdened by immutable issues with scalability, energy inefficiency, and adaptive security. This is an alternative to conventional Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) algorithms that only provide deterministic agreement, but are still expensive, inflexible, and weak under dynamically stable networks. Over the recent years, there has been a lot of development in Machine Learning (ML) and Artificial Intelligence (AI), which have introduced intelligent self-learning consensus mechanisms to increase adaptability, efficiency, and resilience. It highlights the architectural aspects and the operational entities of various ML-powered and hybrid consensus protocols, including PoW–PoS, DPoS–PBFT, and PoCASBFT, as well as their performance implications. Supervised, unsupervised, reinforcement, and federated learning methods are surveyed to provide insights for predictive validation, anomaly detection, energy optimization, and node trust management in blockchain networks [12]. It then compares the performance of its state-of-the-art protocols, demonstrating that ML-assisted hybrids provide 15–40% throughput gains and up to 35% energy savings over the corresponding protocols when trained with traditional models. Ultimately, the paper notes scalability, interpretability, and adversarial ML as the main risks of research in this space, and glances at future directions toward cognitive consensus architectures, also noting that these architectures should be self-healing, context-aware, and able to balance decentralization, performance, and security themselves.