利用动态数据队列和数据熵驱动的参与者选择提高联盟学习的收敛性

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Charuka Herath, Xiaolan Liu, Sangarapillai Lambotharan, Yogachandran Rahulamathavan
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Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection
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来源期刊
IEEE Internet of Things Journal
IEEE Internet of Things Journal Computer Science-Information Systems
CiteScore
17.60
自引率
13.20%
发文量
1982
期刊介绍: The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.
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