{"title":"联邦图学习的自适应客户端选择框架","authors":"Hongli Xu;Xianjun Gao;Jianchun Liu;Qianpiao Ma;Liusheng Huang","doi":"10.1109/TMC.2025.3563404","DOIUrl":null,"url":null,"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.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9760-9773"},"PeriodicalIF":9.2000,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FedACS: An Adaptive Client Selection Framework for Communication-Efficient Federated Graph Learning\",\"authors\":\"Hongli Xu;Xianjun Gao;Jianchun Liu;Qianpiao Ma;Liusheng Huang\",\"doi\":\"10.1109/TMC.2025.3563404\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":50389,\"journal\":{\"name\":\"IEEE Transactions on Mobile Computing\",\"volume\":\"24 10\",\"pages\":\"9760-9773\"},\"PeriodicalIF\":9.2000,\"publicationDate\":\"2025-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Mobile Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10972362/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10972362/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
FedACS: An Adaptive Client Selection Framework for Communication-Efficient Federated Graph Learning
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.
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.