FedACS: An Adaptive Client Selection Framework for Communication-Efficient Federated Graph Learning

IF 9.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongli Xu;Xianjun Gao;Jianchun Liu;Qianpiao Ma;Liusheng Huang
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引用次数: 0

Abstract

Federated graph learning (FGL) has been proposed to collaboratively train the increasing graph data with graph neural networks (GNNs) in a recommendation system. Nevertheless, implementing an efficient recommendation system with FGL still faces two primary challenges, i.e., limited communication bandwidth and non-IID local graph data. Existing works typically reduce communication frequency or transmission amount, which may suffer significant performance degradation under non-IID settings. Furthermore, some researchers propose to share the underlying structure among clients, which brings massive communication cost. To this end, we propose an efficient FGL framework, named FedACS, which adaptively selects a subset of clients for model training, to alleviate communication overhead and non-IID issues simultaneously. In FedACS, the global GNN model learns significant hidden edges and the structure of graph data among selected clients, enhancing recommendation efficiency. This capability distinguishes it from the traditional FL client selection methods. To optimize the client selection process, we introduce a multi-armed bandit (MAB) based algorithm to select participating clients according to the resource budgets and the training performance (i.e., RMSE). Experimental results indicate that FedACS improves RMSE by 5.4% over baselines with the same resource budget and reduces communication costs by up to 70.7% to achieve the same RMSE performance.
联邦图学习的自适应客户端选择框架
联邦图学习(FGL)被提出用于与图神经网络(gnn)协同训练推荐系统中不断增长的图数据。然而,使用FGL实现高效的推荐系统仍然面临两个主要挑战,即有限的通信带宽和非iid局部图数据。现有的工作通常会降低通信频率或传输量,这在非iid设置下可能会导致显著的性能下降。此外,一些研究人员提出在客户端之间共享底层结构,这带来了巨大的通信成本。为此,我们提出了一个高效的FGL框架FedACS,该框架自适应地选择客户端子集进行模型训练,同时减轻了通信开销和非iid问题。在FedACS中,全局GNN模型在选定的客户端中学习重要的隐边和图数据的结构,提高了推荐效率。这种功能将其与传统的FL客户端选择方法区分开来。为了优化客户选择过程,我们引入了一种基于多臂强盗(MAB)的算法,根据资源预算和培训绩效(即RMSE)选择参与客户。实验结果表明,在资源预算相同的情况下,FedACS将RMSE提高了5.4%,并将通信成本降低了70.7%,以达到相同的RMSE性能。
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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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