Optimal policy computing for blockchain based smart contracts via federated learning View Full Text


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Article Info

DATE

2022-07-07

AUTHORS

Wanyang Dai

ABSTRACT

In this paper, we develop a blockchain based decision-making system via federated learning along with an evolving convolution neural net, which can be applied to assemble-to-order services and Metaverses. The design and analysis of an optimal policy computing algorithm for smart contracts within the blockchain will be the focus. Inside the system, each order associated with a demand may simultaneously require multiple service items from different suppliers and the corresponding arrival rate may depend on blockchain history data represented by a long-range dependent stochastic process. The optimality of the computed dynamic policy on maximizing the expected infinite-horizon discounted profit is proved concerning both demand and supply rate controls with dynamic pricing and sequential packaging scheduling in an integrated fashion. Our policy is a pathwise oriented one and can be easily implemented online. The effectiveness of our optimal policy is supported by simulation comparisons. More... »

PAGES

5817-5844

References to SciGraph publications

  • 2021-07-01. Blockchained Adaptive Federated Auto MetaLearning BigData and DevOps CyberSecurity Architecture in Industry 4.0 in PROCEEDINGS OF THE 22ND ENGINEERING APPLICATIONS OF NEURAL NETWORKS CONFERENCE
  • 2019-10-23. Quantum supremacy using a programmable superconducting processor in NATURE
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    http://scigraph.springernature.com/pub.10.1007/s12351-022-00723-z

    DOI

    http://dx.doi.org/10.1007/s12351-022-00723-z

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