区快链排队联邦学习最优决策与元宇宙
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论文发表信息
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英文摘要
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.
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国际会议特邀大会嘉宾主旨报告(Keynote Speeches)
- 该文中的主要学术成就曾作为特邀大会嘉宾主旨报告在国际会议上报告。
- Partial results in the paper were presented as invited keynote talks in
international conferences.
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