Reinforcement Learning-Based Deep FEFM for Blockchain Consensus Mechanism Optimization with Non-Linear Analysis

Abstract

Blockchain technology's efficiency are highly influenced by the applied consensus system. Especially in large-scale networks, conventional consensus algorithms may struggle with issues including too high energy consumption and latency. Combining Reinforcement Learning (RL) with innovative optimisation techniques seems to have major advantages recently shown. Present blockchain consensus approaches define by inefficiencies in resource utilisation and low transaction processing rates. These limitations reduce the real-time speed and scalability of blockchain networks. Maximising these techniques to raise their efficiency while maintaining security presents a huge challenge. This article proposes Reinforcement Learning-Based Deep FEFM (Feature Extraction and Fusion Mechanism), a fresh approach to optimise blockchain consensus processes. The method combines RL with a feature extraction and fusion technique based on deep learning. The deep FEFM enhances the consensus parameter dynamically optimisation learning of blockchain network states. Methods of non-linear analysis are used to assess and enhance the process of optimisation. Experiments on a simulated blockchain network motivated variations in transaction loads and node configurations. The proposed solution reduced latency by 25% and transaction throughput by 35% compared to traditional consensus methods.