最短路径核心选择激励,用于医疗应用的联合学习平台
Yali Jiang1, Haoran Shi1, Zehua Sun1
1School of Software, Shandong University, China.
Computers in biology and medicine
|September 7, 2023
概括
这项研究引入了针对联合学习平台的新激励机制. 它提高了众包数据任务的公平性和效率,确保参与者得到适当的奖励.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 游戏理论 游戏理论
背景情况:
- 联合学习 (FL) 对于解决数据岛屿和从分布式数据中提取价值至关重要.
- 新兴的FL平台通过众包组织用户,设备和数据进行协作任务.
- 现有的机制面临着诸如过度支付和安全漏洞等挑战.
研究的目的:
- 为联合学习平台设计一个激励机制,以改善VCG拍卖.
- 解决VCG机制固有的过度支付,假名攻击和核心偏差问题.
- 确保为联合学习任务参与者提供公平有效的奖励.
主要方法:
- 结合了VCG (Vickrey-Clarke-Groves) 拍卖机制与合作游戏理论的核心概念.
- 介绍了参与者奖励分配的最短路径核心选择策略.
- 在选择参与者和确定奖励时应用VCG-nearest原则.
主要成果:
- 拟议的机制保证了激励的兼容性,个人的合理性和联盟的合理性.
- 它确保在联合学习任务中最大限度地提高社会效率.
- 医学实验结果验证了该机制在现实世界的场景中的有效性.
结论:
- 最短路径核心选择激励机制为联合学习平台提供了强大的解决方案.
- 它增强了经济属性,并减少了任务发行者的支出.
- 该机制提供了更公平的奖励分配,接近VCG结果.
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