通过Fed-GANCC授权精确广告:一种新的联合学习方法,利用生成对抗网络和群组集群
Caiyu Su1, Jinri Wei1, Yuan Lei2
1Guangxi Vocational & Technical Institute of Industry, Nanning, Guangxi, China.
PloS one
|April 10, 2024
概括
本研究介绍了Fed-GANCC,这是一个使用生成对抗网络 (GAN) 和群组集群来改进目标广告的联合学习框架. 美联储GANCC有效地解决了数据隐私,非IID数据和概念漂移问题,优于现有方法.
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:
- 传统的集中式机器学习在有针对性的广告中与数据隐私和非IID数据作斗争.
- 现有的联合学习框架面临着孤立的数据岛屿和概念漂移的挑战.
研究的目的:
- 引入Fed-GANCC,一个创新的联合学习框架,旨在提高定向广告的精度.
- 为了解决数据隐私问题,非独立和相同分布 (非IID) 数据,以及用户行为数据中的概念漂移.
主要方法:
- 将生成对抗网络 (GAN) 与群组集群协同作用.
- 使用对抗生成网络实现用户数据增强算法,以丰富用户行为数据.
- 减轻数据分布不均和概念漂移的影响.
主要成果:
- 与FED-AVG和FED-SGD相比,美联储-GANCC表现优越.
- 观察到精度,损失值和接收器运行特征 (ROC) 指标的显著改进.
- 该框架有效地缓解了孤立数据群岛,非IID数据和概念漂移的问题.
结论:
- 联邦GANCC在联合学习中为目标广告挑战提供了一种新且有效的解决方案.
- 该框架为广告领域的联合学习的未来进展设定了基准.
- 联邦-GANCC为开发高效和先进的联合学习解决方案提供了关键的见解.
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