Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An Attack-Tolerant Trustable Distributed Intelligence Paradigm

Siyuan Chen, Jun Zhang, Yuyang Bai, Peidong Xu, Tianlu Gao, Huaiguang Jiang, Wenzhong Gao, Xiang Li

Research output: Contribution to journalArticlepeer-review

3 Scopus Citations

Abstract

In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of "centralized dispatching and hierarchical management", this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment.
Original languageAmerican English
Pages (from-to)8900-8911
Number of pages12
JournalEnergy Reports
Volume7
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5D00-81745

Keywords

  • attack-tolerant capability
  • blockchain enabled intelligence
  • distributed deep reinforcement learning
  • multi-agents system
  • multi-regional large-scale power system

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