数据交易组合分配与自主经济代理商的联合学习
IEEE transactions on neural networks and learning systems
|November 21, 2023
概括
自主经济代理商 (AEA) 使用联合学习 (FL) 和联合信号处理技术来优化数据交易组合. 这种方法克服了当地信息的局限性,提高了无处不在的情报市场的利能力和效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 数据是无处不在的情报社会中的关键资源.
- 智能数据交易需要自主经济代理 (AEA).
- 局部信息有限阻碍了AEA的投资组合分配模型培训.
研究的目的:
- 为AEA开发一个新的数据交易市场,提供独家本地信息.
- 使AEA能够通过使用联合学习 (FL) 共同培养强大的投资组合分配模型.
- 通过整合定向梯度 (HoG) 和离散波形转换 (DWT) 的直方图来提高收入回报估计.
主要方法:
- 电子投资机构使用联合学习 (FL) 来协同训练投资组合分配模型.
- 使用HoGs和DWT的新组合来表示非静态收入数据.
- 转换域中的本地模型漂移被利用为高效的全球模型更新.
主要成果:
- 拟议的方案在模拟中显示出卓越的性能.
- 测试损失,累计回报,投资组合风险和夏普比率等关键指标得到了改进.
- 观察到,沟通负担减少,培训效率提高.
结论:
- 综合方法有效地解决了数据交易中的信息限制.
- 该方法为优化AEA投资组合分配提供了一个强大的解决方案.
- 这项研究在无处不在的情报环境中推进了智能数据交易.
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