大模型存储定价博弈与区块链联邦学习
- 论文题目
- 英文摘要
We study 2-stage game-theoretic problem oriented 3-stage service policy computing, convolutional neural network (CNN) based algorithm
design, and simulation for a blockchained buffering system with federated learning. More precisely, based on the game-theoretic problem
consisting of both "win-lose" and "win-win" 2-stage competitions, we derive a 3-stage dynamical service policy via a saddle point to a
zero-sum game problem & a Nash equilibrium point to a non-zero-sum game problem. This policy is concerning users-selection, dynamic
pricing, and online rate resource allocation via stable digital currency for the system. The main focus is on the design and analysis
of the joint 3-stage service policy for given queue/environment state dependent pricing and utility functions. The asymptotic optimality
and fairness of this dynamic service policy is justified by diffusion modeling with approximation theory. A general CNN based policy
computing algorithm flow chart along the line of the so-called {\it big model} framework is presented. Simulation case studies are
conducted for the system with three users, where only two of the three users can be selected into the service by a zero-sum dual cost
game competition policy at a time point. Then, the selected two users get into service and share the system rate service resource
through a non-zero-sum dual cost game competition policy. Applications of our policy in the future blockchain based Internet (e.g.,
metaverse & web3.0) and supply chain finance are also briefly illustrated.
- 中文介绍
该文主要研究大模型体系的一般构架，针对金融科技、缓存存储、区块链、联邦学习、博弈论之间的交互展开，各种Token(比如银票，电票，税票等等）
可与NFT及数字货币挂沟，减少光票支付风险，具体引进了动态定价与随机扩散逼近进行了深入合理设计与精准分析。
- 关键词与关键技术
- Game-theoretic scheduling, diffusion approximation, saddle point, Nash equilibrium policy, blockchained queueing buffer system,
federated learning, dynamic resource pricing, stable digital currency
- 一等奖
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