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检索条件"机构=Key Laboratory of Services Computing Technology and System"
1800 条 记 录,以下是231-240 订阅
排序:
Optimizing Model-Driven Federated Learning for Rational and Data-Efficient in Social Mobile Network
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IEEE Network 2024年
作者: Lu, Jianfeng Zhang, Ying Cao, Shuqin Wang, Wei Tang, Changbing Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China
With the rise of data-intensive services in mobile networks, the demand for intelligent and efficient learning frameworks has grown exponentially. Federated Learning (FL), as an emerging paradigm, enables decentralize... 详细信息
来源: 评论
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
arXiv
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arXiv 2024年
作者: Hu, Gangqiang Lu, Jianfeng Han, Jianmin Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Zhejiang Normal University China School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Universityof Science and Technology China
Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context... 详细信息
来源: 评论
Robust Beamforming for Downlink Multi-Cell systems: A Bilevel Optimization Perspective
arXiv
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arXiv 2024年
作者: Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China
Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, suc... 详细信息
来源: 评论
Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender systems
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IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页
作者: Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia
In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also havi... 详细信息
来源: 评论
Using Experience Classification for Training Non-Markovian Tasks
SSRN
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SSRN 2023年
作者: Miao, Ruixuan Lu, Xu Tian, Cong Yu, Bin Cui, Jin Duan, Zhenhua Institute of Computing Theory and Technology State Key Laboratory of Integrated Services Networks Xidian University China School of Computer Science Xi’an Shiyou University China
Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, freq... 详细信息
来源: 评论
Sustainable and Trusted Vehicular Energy Trading Enabled by Scalable Blockchains
Sustainable and Trusted Vehicular Energy Trading Enabled by ...
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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)
作者: Qingmei Yang Lijun Sun Xiao Chen Lingling Wang College of Information Science and Technology Qingdao University of Science and Technology Shandong China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Computing and Mathematical Sciences University of Leicester Leicester UK
The integration of electric vehicles (EVs) into the transportation network has positioned their battery packs not only as power sources for mobility but also as crucial components of energy storage. This dual role ena... 详细信息
来源: 评论
Byzantine-Robust Hierarchical Aggregation for Cross-Device Federated Learning in Consumer IoT
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IEEE Transactions on Consumer Electronics 2024年 1-1页
作者: Liu, Jingwei Wu, Yufeng Du, Wei Sun, Rong Xu, Guangxia Liu, Lei Wu, Celimuge Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi’an China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Guangzhou Institute of Technology Xidian University Guangzhou China Meta-Networking Research Center University of Electro-Communications Tokyo Japan
Nowadays, Federated Learning (FL) has emerged as a prominent technique of model training in Consumer Internet of Things (CIoT) without sharing sensitive local data. Targeting privacy leakage of cross-device FL in CIoT... 详细信息
来源: 评论
Low-Complexity and Interpretable Machine Learning Method for Multiple Parameters Estimation
Low-Complexity and Interpretable Machine Learning Method for...
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OptoElectronics and Communications Conference, OECC
作者: Zhiquan Wan Zhenming Yu Kun Xu Kun Yin Hui Yu Chuliang Hu Zhejiang Lab Research Center of High Efficiency Computing System Hangzhou China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Sciences Institute of Innovative Computing Technology Hangzhou China
We experimentally demonstrate a random forest- based method for simultaneous modulation format identification and OSNR monitoring. Compared with neural network-based method, this method is interpretable and reduces co... 详细信息
来源: 评论
Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms
Last-X-Generation Archiving Strategy for Multi-Objective Evo...
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Congress on Evolutionary Computation
作者: Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China
For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set sele... 详细信息
来源: 评论
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
arXiv
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arXiv 2023年
作者: Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab
Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious ... 详细信息
来源: 评论