在协作培训市场上选择风险的资源交易策略
Quyuan Wang1, Xinyu Ni1, Yuping Tu1
1Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology, Chongqing Technology and Business University, Chongqing, China.
PloS one
|July 21, 2025
概括
本研究介绍了边缘计算和人工智能的协作培训资源分配的经济策略. 它为预算优化和市场平衡提出了新的算法,提高了联合培训的效率和稳定性.
科学领域:
- 边缘计算 边缘计算
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 边缘计算和人工智能的协作培训正在获得引力.
- 现有的研究主要集中在技术资源分配上,忽视了经济交易机制.
研究的目的:
- 在联合培训中研究有效的预算分配策略,用于计算和数据资源.
- 在协作培训环境中开发资源交易和市场平衡的经济模型.
主要方法:
- 使用恒定比例投资组合投资,在预算限制下最大限度地实现回报.
- 采用Swing梯度搜索算法来确定最佳的资源采集策略.
- 开发了渐进和平滑的定价算法,以保持市场平衡.
主要成果:
- 提出的策略和算法显示出高算法效率和战略有效性.
- 经济框架成功地解决了资源采购量之间的合问题.
- 定价算法有效地保持了动态市场平衡.
结论:
- 该研究为协作培训的资源配置提供了一个强大的经济框架.
- 开发的算法为优化预算分配和市场稳定提供了实际解决方案.
- 这项研究在边缘人工智能联合培训中弥合了技术实施和经济可行性之间的差距.
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