FedART:一个神经模型,集成联合学习和自适应共振理论
Shubham Pateria1, Budhitama Subagdja1, Ah-Hwee Tan1
1School of Computing and Information Systems, Singapore Management University, Singapore.
概括
联邦学习 (FL) 努力处理异质数据. 我们的新联合适应共振理论 (FedART) 方法使用类别代码来实现更好的全球模型聚合,优于现有的FL方法.
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:
- 联合学习 (FL) 允许在保护数据隐私的同时进行协作培训.
- 当前的FL方法平均模型参数,导致对异质客户端数据的偏差.
- 现有的FL方法仅限于监督分类,不支持无监督集群.
研究的目的:
- 提出一种新的一次性联合学习方法,联合适应共振理论 (FedART).
- 解决现有的FL方法在数据异质性和任务范围方面的局限性.
- 为了使FL能够进行分类和聚类任务,即使使用不同的客户端数据.
主要方法:
- FedART使用自组织的自适应共振理论 (ART) 模型来学习代表数据集群的类别代码.
- 客户将私人数据与本地类别代码联系起来,这些代码代表异质数据分布.
- 全球模型将这些本地代码汇总成全球类别代码,保留来自异质数据的信息.
主要成果:
- 通过聚合类别代码而不是参数,FedART有效地处理跨客户端的异质数据分布.
- 拟议的方法支持联合分类和无监督集群任务.
- 实验结果表明,在异质数据集上,FedART的性能优于最先进的FL方法.
结论:
- FedART提供了一个强大的解决方案,用于使用异质数据进行联合学习.
- 这种方法扩大了FL的适用于无监督集群的应用范围.
- 在保护隐私的协作机器学习中,FedART代表了重大进步.
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