基于拍卖的联合学习的成本意识实用性最大化投标策略
IEEE transactions on neural networks and learning systems
|November 6, 2024
概括
本研究介绍了基于拍卖的联合学习 (AFL) 的联合成本意识竞标策略. 这种新方法有助于数据消费者最大限度地提高效用,并提高FL模型在一般化第二价拍卖中的准确性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 基于拍卖的联合学习 (AFL) 激励数据贡献.
- 现有的方法忽略了通用第二价格 (GSP) 拍卖的成本机制.
- 数据消费者 (DC) 在竞争激烈的AFL市场中需要有效的竞标策略.
研究的目的:
- 建议在基于GSP拍卖的FL中为DC提供一种新的联合成本意识的投标策略.
- 为了使 DC 能够最大限度地提高其效用,并提高 FL 模型的准确性.
- 解决AFL数据采集最佳竞标的悬而未决的问题.
主要方法:
- 根据GSP拍卖规则制定了最佳出价函数.
- 开发了一个框架,共同优化公用事业估计和市场价格建模.
- 实施基于投资回报率 (ROI) 的方法来确定最佳的投标价格.
主要成果:
- 拟议的战略显著优于八种最先进的方法.
- 在数据采集,成本效益,实用性和FL模型准确性方面取得了平均改进.
- 在六个基准数据集中表现出卓越的性能.
结论:
- 联邦成本意识的投标策略有效地最大化了基于GSP的AFL的DC公用事业.
- 该方法提高了FL模型的整体性能和数据采集效率.
- 这项工作为竞争激烈的AFL市场动态提供了关键的解决方案.
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