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检索条件"主题词=distributed databases"
10460 条 记 录,以下是151-160 订阅
排序:
Enhancing Federated Learning Convergence With Dynamic Data Queue and Data-Entropy-Driven Participant Selection
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IEEE INTERNET OF THINGS JOURNAL 2025年 第6期12卷 6646-6658页
作者: Herath, Charuka Liu, Xiaolan Lambotharan, Sangarapillai Rahulamathavan, Yogachandran Loughborough Univ London Inst Digital Technol London E20 3BS England
Federated learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost e... 详细信息
来源: 评论
Identifiability in Dynamic Acyclic Networks With Partial Excitation and Measurement
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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年 第4期70卷 2305-2320页
作者: Cheng, Xiaodong Shi, Shengling Lestas, Ioannis Hof, Paul M. J. Van den Wageningen Univ & Res Dept Plant Sci Math & Stat Methods Biometris NL-6700 AA Wageningen Netherlands MIT Dept Chem Engn Cambridge MA 02139 USA Univ Cambridge Dept Engn Control Grp Cambridge CB2 1PZ England Eindhoven Univ Technol Dept Elect Engn Control Syst Grp NL- 5600 MB Eindhoven Netherlands
This article deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considering an acyclic network where only a s... 详细信息
来源: 评论
Task-Aware Data Selectivity in Pervasive Edge Computing Environments
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年 第1期37卷 513-525页
作者: Koukosias, Athanasios Anagnostopoulos, Christos Kolomvatsos, Kostas Univ Thessaly Dept Informat & Telecommun Volos 38221 Greece Univ Glasgow Sch Comp Sci Glasgow City G12 8QQ Scotland
Context-aware data selectivity in Edge Computing (EC) requires nodes to efficiently manage the data collected from Internet of Things (IoT) devices, e.g., sensors, for supporting real-time and data-driven pervasive an... 详细信息
来源: 评论
Privacy-Aware Data Acquisition Under Data Similarity in Regression Markets
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页
作者: Pandey, Shashi Raj Pinson, Pierre Popovski, Petar Aalborg Univ Dept Elect Syst Connect Sect DK-9220 Aalborg Denmark Imperial Coll London Dyson Sch Design Engn London SW7 2AZ England Tech Univ Denmark Dept Technol Management & Econ DK-2800 Lyngby Denmark Halfspace DK-1114 Copenhagen Denmark
Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences and data similarity among data ... 详细信息
来源: 评论
Rethinking RAN Architecture for Deep Fusion of AI and Communication in 6G
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IEEE WIRELESS COMMUNICATIONS 2025年 第3期32卷 164-174页
作者: Li, Nan Wang, Yang Sun, Qi Li, Xiang Huang, Jinri Liu, Chunhui Huang, Yuhong Hu, Zhenping Han, Yantao I, Chih-lin Beijing Univ Posts & Telecommun Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China China Mobile Res Inst Dept Wireless & Terminal Technol Beijing Peoples R China China Mobile Res Inst Wireless & Device Technol Res Off Beijing Peoples R China ZGC Inst Ubiquitous X Innovat & Applicat Beijing Peoples R China China Mobile Commun Grp Co Ltd Beijing Peoples R China China Mobile Commun Grp Co Ltd Sci & Innovat Planning Div Sci & Technol Innovat Dept Beijing Peoples R China
The deep integration of artificial intelligence (AI) with the radio access network (RAN) is envisioned to revolutionize mobile communications as we progress toward 6G. This article first explores the enhancement of th... 详细信息
来源: 评论
MimoSketch: A Framework for Frequency-Based Mining Tasks on Multiple Nodes With Sketches
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年 第3期37卷 1311-1324页
作者: Wu, Wenfei Xu, Yuchen Peking Univ Sch Comp Sci Beijing 100871 Peoples R China
In distributed data stream mining, we abstract a MIMO scenario where a stream of multiple items is mined by multiple nodes. We design a framework named MimoSketch for the MIMO-specific scenario, which improves the fun... 详细信息
来源: 评论
AIEA: An Asynchronous Influence-Based Evolutionary Algorithm for Expensive Many-Objective Optimization
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IEEE TRANSACTIONS ON CYBERNETICS 2025年 第2期55卷 786-799页
作者: Wei, Feng-Feng Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Buildingand Urban Sci Guangzhou 510006 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea
In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computation... 详细信息
来源: 评论
Online Management for Edge-Cloud Collaborative Continuous Learning: A Two-Timescale Approach
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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年 第12期23卷 14561-14574页
作者: Lin, Shaohui Zhang, Xiaoxi Li, Yupeng Joe-Wong, Carlee Duan, Jingpu Yu, Dongxiao Wu, Yu Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Hong Kong Baptist Univ Dept Interact Media Kowloon Tong Hong Kong Peoples R China Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Pengcheng Lab Dept Commun Shenzhen 518066 Peoples R China Shandong Univ Inst Intelligent Comp Sch Comp Sci & Technol Qingdao 266237 Peoples R China Dongguan Univ Technol Sch Cyberspace Secur Dongguan 523808 Peoples R China
Deep learning (DL) powered real-time applications usually need continuous training using data streams generated over time and across different geographical locations. Enabling data offloading among computation nodes t... 详细信息
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Order Optimal Bounds for One-Shot Federated Learning Over Non-Convex Loss Functions
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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年 第4期70卷 2807-2830页
作者: Sharifnassab, Arsalan Salehkaleybar, Saber Golestani, S. Jamaloddin Univ Alberta Comp Sci Dept Reinforcement Learning & Artificial Intelligence R Edmonton AB T6G 2E8 Canada Leiden Univ Leiden Inst Adv Comp Sci LIACS NL-2311 EZ Leiden Netherlands Sharif Univ Technol Dept Elect Engn Tehran 113659466 Iran
We consider the problem of federated learning in a one-shot setting in which there are m machines, each observing n sample functions from an unknown distribution on non-convex loss functions. Let F:[-1, 1](d )-> R ... 详细信息
来源: 评论
Accelerating distributed Repartition Joins on Skewed Datasets via Patch-Based Shuffling
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IEEE ACCESS 2025年 13卷 41068-41096页
作者: Kassela, Evdokia Konstantinou, Ioannis Koziris, Nectarios Natl & Tech Univ Athens Sch Elect & Comp Engn Athens 11527 Greece Univ Thessaly Dept Informat & Telecommun Lamia 35100 Greece
In distributed workloads involving joins and aggregations, skewed attribute values often cause load balancing issues, leading to stragglers and increased execution times. Existing solutions often rely on cost-based mo... 详细信息
来源: 评论