非iid数据集上层次结构中的顺序联邦学习

IF 9.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xingrun Yan;Shiyuan Zuo;Rongfei Fan;Han Hu;Li Shen;Puning Zhao;Yong Luo
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引用次数: 0

摘要

在真正的联邦学习(FL)系统中,在客户机和参数服务器(PS)之间传递模型参数的通信开销通常是一个瓶颈。分层联邦学习(HFL)在客户端和节点之间设置了多个边缘服务器(ESs),可以部分缓解通信压力,但仍然需要在节点上聚集多个边缘服务器的模型参数。为了进一步减少通信开销,我们删除了中心节点,使得每次迭代只通过在相邻的两个节点之间传输全局模型来完成模型训练。我们称这种连续学习方法为顺序FL (Sequential FL)。我们首次将SFL引入到HFL中,并提出了一种适应这种组合框架的新算法,称为Fed-CHS。在不同的数据异构设置下,得到了强凸和非凸损失函数的收敛结果,与单独的HFL或SFL算法具有相当的收敛性能。实验结果证明了我们提出的Fed-CHS在节省通信开销和测试精度方面优于基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical federated learning (HFL) that poses multiple edge servers (ESs) between clients and the PS can partially alleviate communication pressure but still needs the aggregation of model parameters from multiple ESs at the PS. To further reduce communication overhead, we remove the central PS, so that each iteration only completes model training by transmitting the global model between two adjacent ES. We call this serial learning method Sequential FL (SFL). For the first time, we introduced SFL into HFL and proposed a novel algorithm adapted to this combined framework, called Fed-CHS. Convergence results are derived for strongly convex and non-convex loss functions under various data heterogeneity setups, which show comparable convergence performance with the algorithms for HFL or SFL solely. Experimental results provide evidence of the superiority of our proposed Fed-CHS on both communication overhead saving and test accuracy over baseline methods.
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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