Graph-based Hierarchical Deep Reinforcement Learning for Deliverable Block Propagation with Optimal Hybrid Cost in Web 3.0

TL;DR

This paper introduces a graph-based hierarchical deep reinforcement learning framework to optimize block propagation in consortium blockchain Web 3.0, balancing timeliness and delivery coverage efficiently.

Contribution

It proposes a novel delivery-aware metric and a joint optimization framework using GHDRL with graph neural networks for improved block propagation.

Findings

GHDRL achieves up to 19.2% lower hybrid cost than baselines.

The model generalizes from 100-peer to 500-peer networks without retraining.

Numerical results validate the effectiveness across different network scales.

Abstract

Web 3.0 is envisioned as a decentralized paradigm, where blockchain serves as a core technology for transparent and tamper-proof data management. Among various blockchain architectures, consortium blockchains have emerged as the preferred platform for enterprise-grade Web 3.0. For consortium blockchains, newly generated blocks are generally propagated to all consensus nodes for validation through the gossip protocol. However, gossip-based propagation may introduce substantial message redundancy and tail latency. Moreover, the consensus nodes exhibit heterogeneous availability patterns, and existing block propagation schemes often overlook such temporal constraints. Therefore, the joint optimization of propagation timeliness and delivery coverage remains an open problem. In this paper, we propose a deliverable block propagation optimization framework for consortium blockchain-enabled Web 3.0. We first propose a delivery-aware timeliness metric called Age of Validated Block (AoVB), which excludes block receptions occurring outside the availability window of each consensus node, thereby measuring only actionable synchronization latency. This metric is unified with the block arrival rate into a hybrid cost objective that balances timeliness against delivery. To solve this complex optimization problem, we propose a Graph-based Hierarchical Deep Reinforcement Learning (GHDRL) method, which comprises a graph isomorphism network-based assignment module and a graph attention network-based propagation module. The two modules are optimized jointly under a two-stage training strategy. Numerical results show that GHDRL consistently outperforms all compared schemes across network scales from 50 to 500 peers, achieving up to 19.2% lower hybrid cost than the best-performing neural baseline. Moreover, the model generalizes from 100-peer training instances to 500-peer deployments without retraining.