A Distributed and Privacy-Aware High-Throughput Transaction Scheduling Approach for Scaling Blockchain

Xiaoyu Qiu, Wuhui Chen*, Bingxin Tang, Junyuan Liang, Hong Ning Dai, Zibin Zheng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)


Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.

Original languageEnglish
Pages (from-to)4372-4386
Number of pages15
JournalIEEE Transactions on Dependable and Secure Computing
Issue number5
Early online date25 Oct 2022
Publication statusPublished - 1 Sept 2023

Scopus Subject Areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Blockchain
  • deep reinforcement learning
  • distributed training
  • privacy-aware
  • transaction scheduling


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