Clustered federated learning enhanced by DAG-based blockchain with adaptive tip selection algorithm

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaofeng Xue , Haokun Mao , Qiong Li , Xin Guan
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引用次数: 0

Abstract

Federated learning (FL) enables machine learning on distributed data while preserving client privacy. However, FL faces challenges such as device heterogeneity, central server vulnerabilities, and non-independent and identically distributed data. To address these challenges, researchers proposed an asynchronous and decentralized clustered FL (CFL) using a directed acyclic graph (DAG)-based blockchain, called specializing DAG FL (SDAGFL). However, SDAGFL consumes high communication and storage resources, posing a substantial burden on devices with limited resources. To overcome these limitations, we propose a novel CFL framework called DAG-CFL. DAG-CFL consists of a server layer with multiple servers implementing DAG-based blockchain and a client layer. Within this framework, we propose an adaptive tip selection algorithm (ATSA) to select the most suitable tip nodes for model aggregation. The analysis indicates that DAG-CFL significantly reduces communication and storage resource consumption on the client side compared with SDAGFL. In addition, the convergence of DAG-CFL and the time and space complexity of ATSA are analyzed to show the effectiveness of DAG-CFL. We evaluate DAG-CFL and ATSA on cluster-wise MNIST and CIFAR-10 datasets. The results show that DAG-CFL achieves comparable performance to the best CFL baseline method while eliminating the need for a predefined number of clusters. Notably, DAG-CFL achieves an 8% increase in accuracy compared with SDAGFL. The experiment results also show the robustness of DAG-CLF in various data distribution shift scenarios and indicate that ATSA can effectively cluster clients with a modularity value of 0.66 for the MNIST dataset and 0.71 for the CIFAR-10 dataset.

Abstract Image

基于dag的区块链自适应尖端选择算法增强的聚类联邦学习
联邦学习(FL)支持在分布式数据上进行机器学习,同时保护客户端隐私。但是,FL面临着设备异构、中心服务器漏洞、数据非独立且分布相同等挑战。为了解决这些挑战,研究人员提出了一种异步和分散的集群FL (CFL),使用基于有向无环图(DAG)的区块链,称为专用DAGFL (SDAGFL)。然而,SDAGFL消耗了大量的通信和存储资源,对资源有限的设备造成了很大的负担。为了克服这些限制,我们提出了一种新的CFL框架,称为DAG-CFL。DAG-CFL由一个服务器层和一个客户端层组成,其中有多个服务器实现基于dag的区块链。在此框架内,我们提出了一种自适应尖端选择算法(ATSA)来选择最合适的尖端节点进行模型聚合。分析表明,与SDAGFL相比,DAG-CFL显著降低了客户端的通信和存储资源消耗。此外,分析了DAG-CFL的收敛性和ATSA的时空复杂度,证明了DAG-CFL的有效性。我们在MNIST和CIFAR-10数据集上对DAG-CFL和ATSA进行了评估。结果表明,DAG-CFL达到了与最佳CFL基线方法相当的性能,同时消除了对预定义簇数的需求。值得注意的是,与SDAGFL相比,DAG-CFL的精度提高了8%。实验结果还显示了DAG-CLF在各种数据分布转移场景下的鲁棒性,并表明ATSA可以有效地聚类客户端,MNIST数据集的模块化值为0.66,CIFAR-10数据集的模块化值为0.71。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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