D2 联邦:联合半监督学习与双重角色添加本地培训和双重视角全球聚合
概括
联合半监督学习 (FSSL) 通过使用未标记的数据来改进联合学习 (FL). 我们的D2Fed方法减轻了干扰,并增强了对更好的全球模型的聚合,而不会损失隐私.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合半监督学习 (FSSL) 利用未标记的数据来改善联合学习 (FL).
- 现有的FSSL方法在标记和未标记客户端之间存在差异,导致在本地培训期间的任务间干扰.
- 目前在FSSL中的聚合策略通常是单一的视角,主要关注数据量 (例如,FedAvg).
研究的目的:
- 提出一种新的FSSL方法,D2Fed,它解决了任务间干扰,并增强了模型聚合.
- 为了减轻与完全标记和完全没有标记的客户学习全球模型所产生的挑战.
- 开发一个更全面的聚合策略,超越数据量意识的方法.
主要方法:
- D2Fed采用双角色添加局部培训 (DALT) 来区分标记和未标记客户端的角色,减少任务间干扰.
- 引入了双视角全球聚合 (DGA) 策略,整合了客户类型意识和数据量意识聚合.
- 该方法在各种数据集上进行评估,包括CIFAR-10/100,SVHN,FMNIST和STL-10.
主要成果:
- 在各种数据设置下,D2Fed在五个数据集中显著超过了最先进的方法.
- 拟议的DALT和DGA战略有效地改善了当地培训和全球模型聚合.
- 实验结果证实了D2Fed在FSSL任务中的有效性和稳定性.
结论:
- 通过解决当地培训和全球聚合的关键挑战,D2Fed为联合半监督学习提供了有效的解决方案.
- 该方法可以提高模型性能,而不会影响数据隐私.
- D2Fed代表了FSSL的重大进步,提供了一种更强大,更全面的方法.
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