一个基于转移学习和知识蒸的联合学习框架,用于有针对性的广告.
Caiyu Su1, Jinri Wei1, Yuan Lei2
1Guangxi Vocational & Technical Institute of Industry, Nanning, Guangxi, China.
PeerJ. Computer science
|September 14, 2023
概括
本研究引入了使用C-means集群和动态选择的新框架,以克服数据隐私问题和定向广告中的模型异质性,提高数据共享效率和准确性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 定向广告面临着由于隐私数据泄露和广告商不愿分享信息的挑战.
- 这导致了孤立的数据岛屿和模型异质性,阻碍了有效的数据利用.
研究的目的:
- 开发一个安全高效的数据共享框架,用于定向广告.
- 在联合学习环境中解决数据岛屿和模型异质性挑战.
主要方法:
- 基于最大平均差的C-means集群算法被提议用于评估参数分布差异.
- 引入了一种创新的动态选择算法,包括知识蒸和重量校正,以减轻模型异质性.
主要成果:
- 与现有模型相比,拟议的框架表现出优越的性能.
- 使用精度,损失和AUC (ROC曲线下的面积) 进行评估,该框架实现了更高的精度,更低的损失和更好的AUC.
- 该框架保持了具有相当计算时间的性能效率.
结论:
- 开发的框架为数据共享提供了更可靠,可控制和安全的方法.
- 这提高了目标广告系统的整体效率和准确性.
- 这项研究为广告中的保护隐私的数据协作提供了强有力的解决方案.
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