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DFedADMM:对于分散式联合学习的双重约束控制模型不一致性
概括
分散式联合学习 (DFL) 方法得到了DFedADMM和DFedADMM-SAM的改进,它们解决了局部不一致性和过度拟合. 这些新的算法提高了概括性,并减少了去中心化网络中的通信开销.
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:
- 联邦学习 (FL) 面临着沟通负担.
- 分散联合学习 (DFL) 移除了中央服务器,但与本地不一致性和异质过度装配作斗争.
研究的目的:
- 提出新的DFL算法,DFedADMM和DFedADMM-SAM,以提高DFL的性能.
- 解决DFL地区局部不一致性和局部异质过度装配的挑战.
主要方法:
- DFedADMM使用双变量初级双优化 (ADMM) 来管理来自异质数据的模型不一致性.
- DFedADMM-SAM结合了敏度感知最小化 (SAM) 来通过生成局部平面模型来减轻过拟合.
主要成果:
- 在非凸的设置中,对于DFedADMM和DFedADMM-SAM都得出了理论收率.
- 在MNIST,CIFAR10和CIFAR100数据集上的实证验证显示出卓越的性能.
结论:
- DFedADMM和DFedADMM-SAM显著提高了泛化,融合速度,并减少了DFL中的通信开销.
- 提出的方法为分散式机器学习中持续存在的挑战提供了有效的解决方案.
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