辅助分类器生成对抗网络的联合转移学习:框架和工业应用
Wei Guo1, Yijin Wang1, Xin Chen1
1State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China.
概括
本研究介绍了使用辅助分类器生成对抗网络 (ACGAN-FTL) 的联合转移学习 (FL) 框架,以实现制造业的个性化机器学习模型,同时保持数据隐私. 与基线方法相比,ACGAN-FTL框架实现了显著的性能改进.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 数据隐私问题阻碍了工业环境中的协作模式培训.
- 分散的数据岛阻碍了有效的个性化模型的创建.
- 现有的方法努力平衡个性化与严格的数据隐私要求.
研究的目的:
- 提出一个新的联邦转移学习 (FL) 框架,用于在制造业中保护隐私的个性化机器学习.
- 解决在孤立的工业数据集上培训个性化模型的挑战.
- 在现实世界的工业场景中提高机器学习模型的性能.
主要方法:
- 开发了一个联合转移学习 (FL) 框架,集成辅助分类器生成对抗网络 (ACGAN).
- 利用联合学习 (FL) 为保护隐私的全球模式培训在分散的数据.
- 员工转移学习 (TL) 调整全球模型以实现个性化应用.
- 利用ACGAN生成合成数据,在保持隐私的同时将FL和TL连接起来.
主要成果:
- 在碳阳极质量预测任务中,ACGAN-FTL框架实现了0.81准确度,0.86精度,0.74回忆率和0.79F1得分.
- 与没有FL和TL的基线方法相比,表现出显著的性能增长,增加了13% (精度),11% (精度),16% (回忆) 和15% (F1评分).
- 在整个机器学习过程中确保数据隐私.
结论:
- 拟议的ACGAN-FTL框架有效地使个人化机器学习能够在工业环境中保护隐私.
- 该框架成功地解决了孤立数据岛屿和隐私问题的局限性.
- ACGAN-FTL满足现实世界工业应用的性能要求,与传统方法相比,提供了显著的改进.
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