融合通道个性标准化的本地自适应联邦学习研究
Local Adaptive Federated Learning with Channel Personalized Normalization
赵宇 1陈思光1
作者信息
- 1. 南京邮电大学物联网学院 南京 210003
- 折叠
摘要
为了缓解联邦学习(FL)中客户端之间由于完全重叠特征偏移所带来的数据异构问题影响,该文提出一种融合通道个性标准化的本地自适应联邦学习算法.具体地,构建了一个面向数据特征偏移的联邦学习模型,在训练开始之前先对客户端中的图像数据集进行一系列随机增强操作.其次,客户端分别按颜色通道单独计算数据集的均值和标准差,实现通道个性标准化.进一步地,设计本地自适应更新联邦学习算法,即自适应地聚合全局模型和本地模型以进行本地初始化,该聚合方法的独特之处在于既保留了客户端模型的个性化特征,同时又能从全局模型中捕获必要信息,以提升模型的泛化性能.最后,实验结果表明,该文所提算法与现有相关算法相比,收敛速度更快,准确率提高了3%～19%.
Abstract
To relieve the impact of data heterogeneity problems caused by full overlapping attribute skew between clients in Federated Learning(FL),a local adaptive FL algorithm that incorporates channel personalized normalization is proposed in this paper.Specifically,an FL model oriented to data attribute skew is constructed,and a series of random enhancement operations are performed on the images data set in the client before training begins.Next,the client calculates the mean and standard deviation of the data set separately by color channel to achieve channel personalized normalization.Furthermore,a local adaptive update FL algorithm is designed,that is,the global model and the local model are adaptively aggregated for local initialization.The uniqueness of this aggregation method is that it not only retains the personalized characteristics of the client model,but also can capture necessary information in the global model to improve the generalization performance of the model.Finally,the experimental results demonstrate that the proposed algorithm obtains competitive convergence speed compared with existing representative works and the accuracy is 3%～19%higher.
关键词
边缘计算/联邦学习/标准化/模型聚合Key words
Edge computing/Federated Learning(FL)/Normalization/Model aggregation引用本文复制引用
基金项目
国家自然科学基金(61971235)
江苏省"333高层次人才培养工程"()
南邮"1311"人才计划()
出版年
2024
NETL
NSTL
万方数据