APCSMA:适应型个性化客户端选择和模型聚合算法,用于边缘计算场景中的联合学习
Xueting Ma1,2, Guorui Ma1, Yang Liu3
1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.
Entropy (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了一种自适应个性化客户端选择和模型聚合算法 (APCSMA),以改善边缘计算中的联合学习 (FL). APCSMA通过自适应地选择客户并有效地汇总他们的贡献来提高模型准确性.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 边缘计算 边缘计算
背景情况:
- 集中式机器学习面临着大数据集的挑战.
- 联合学习 (FL) 提供保护隐私的分布式培训.
- 边缘计算中的客户端异质性阻碍了FL的性能.
研究的目的:
- 引入一个自适应的个性化客户端选择和模型聚合算法 (APCSMA).
- 在异质边缘计算环境中优化FL性能.
- 解决客户端异质性对模型准确性的影响.
主要方法:
- 开发了APCSMA以使用本地模型性能和等号相似性来评估客户贡献.
- 设计了一个ContriFunc来量化客户贡献的选择和聚合.
- 实现了个性化的本地模型更新,而不是直接的全球模型覆盖.
主要成果:
- 在FashionMNIST和Cifar-10数据集上的实验表明了准确度的提高.
- 在不同的数据分布中,FashionMNIST的准确性增长为3.9%,1.9%和1.1%.
- 在Cifar-10的测试中,分别获得了31.9%,8.4%和5.4%的显著精度提升.
结论:
- 在边缘计算设置中,APCSMA有效地提高了FL性能.
- 该算法成功地减轻了客户端异质性的负面影响.
- 个性化更新和自适应聚合导致更高的模型准确性.
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