Search Results - "Zhan, Yufeng"

A Deep Reinforcement Learning Based Offloading Game in Edge Computing by Zhan, Yufeng, Guo, Song, Li, Peng, Zhang, Jiang

“…Edge computing is a new paradigm to provide strong computing capability at the edge of pervasive radio access networks close to users. A critical research…”
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A Learning-Based Incentive Mechanism for Federated Learning by Zhan, Yufeng, Li, Peng, Qu, Zhihao, Zeng, Deze, Guo, Song

“…Internet of Things (IoT) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection,…”
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A Survey of Incentive Mechanism Design for Federated Learning by Zhan, Yufeng, Zhang, Jie, Hong, Zicong, Wu, Leijie, Li, Peng, Guo, Song

“…Federated learning is promising in enabling large-scale machine learning by massive clients without exposing their raw data. It can not only enable the clients…”
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Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach by Liu, Yang, Xu, Changqiao, Zhan, Yufeng, Liu, Zhixin, Guan, Jianfeng, Zhang, Hongke

“…IoT-based services benefit from cloud which offers a virtually unlimited capabilities, such as storage, processing, and communication. However, the challenges…”
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Sinus rhythm restoration reverses tricuspid regurgitation in patients with atrial fibrillation: a systematic review and meta-analysis by Zhan, Yufeng, Li, Ning

“…Tricuspid regurgitation (TR) is a common valvular heart disease worldwide, and current guidelines for TR treatment are relatively conservative, as well as with…”
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Incentive-Aware Time-Sensitive Data Collection in Mobile Opportunistic Crowdsensing by Zhan, Yufeng, Xia, Yuanqing, Liu, Yang, Li, Fan, Wang, Yu

“…Data collection is a crucial operation in mobile opportunistic crowdsensing. The design of data collection is challenging due to the fact that smart devices…”
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A cost and makespan aware scheduling algorithm for dynamic multi-workflow in cloud environment by Xia, Yuanqing, Zhan, Yufeng, Dai, Li, Chen, Yuehong

“…With the development of cloud computing, a growing number of workflows are deployed on cloud platform that can dynamically provides cloud resources on demand…”
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A survey on deploying mobile deep learning applications: A systemic and technical perspective by Wang, Yingchun, Wang, Jingyi, Zhang, Weizhan, Zhan, Yufeng, Guo, Song, Zheng, Qinghua, Wang, Xuanyu

“…With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple…”
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Incentive Mechanism Design in Mobile Opportunistic Data Collection With Time Sensitivity by Zhan, Yufeng, Xia, Yuanqing, Zhang, Jinhui, Wang, Yu

“…Mobile crowdsensing systems aim at providing various novel sensing applications by recruiting pervasive users with mobile devices, which are now equipped with…”
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Deadline-Constrained and Cost-Effective Multi-Workflow Scheduling with Uncertainty in Cloud Control Systems by Ye, Lingjuan, Yang, Liwen, Xia, Yuanqing, Zhan, Yufeng, Zhao, Xinchao

“…In cloud control systems, generating an efficient and economical workflow scheduling strategy for deadline-constrained workflow applications, especially in…”
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Long Non-coding RNA LINC01119 Promotes Neuropathic Pain by Stabilizing BDNF Transcript by Zhang, Le, Feng, Hao, Jin, Yanwu, Zhan, Yufeng, Han, Qi, Zhao, Xin, Li, Peilong

“…Neuropathic pain (NP) is caused by primary injury or dysfunction of the peripheral and the central nervous system. Long non-coding RNAs were critical…”
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Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning by Zhan, Yufeng, Li, Peng, Guo, Song

“…Federated learning is promising in enabling large-scale machine learning by massive mobile devices without exposing the raw data of users with strong privacy…”
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Multi-delay network based IEEE 802.15.4 for low delay deterministic networked control systems by Aopeng Song, Yuanqing Xia, Yufeng Zhan

“…Wireless networked control systems have gained significant popularity due to commissioning and maintenance ease. IEEE 802.15.4 standard is one of wireless…”
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A new GTS allocation scheme in IEEE 802.15.4 sensor-actuator networks by Zhan, Yufeng, Xia, Yuanqing

“…Due to construction and maintenance ease, wireless networked control systems have gained a lot of popularity. Currently a lot of wireless communication…”
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An Incentive Mechanism Design for Efficient Edge Learning by Deep Reinforcement Learning Approach by Zhan, Yufeng, Zhang, Jiang

“…Emerging technologies and applications have generated large amounts of data at the network edge. Due to bandwidth, storage, and privacy concerns, it is often…”
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Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach by Liu, Chi Harold, Chen, Zheyu, Zhan, Yufeng

“…High-quality data collection is crucial for mobile crowd sensing (MCS) with various applications like smart cities and emergency rescues, where various…”
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Total Unimodularity and Strongly Polynomial Solvability of Constrained Minimum Input Selections for Structural Controllability: An LP-Based Method by Zhang, Yuan, Xia, Yuanqing, Zhan, Yufeng

“…This article investigates several cost-sparsity-induced optimal input selection problems for structured systems. Given an autonomous system and a prescribed…”
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On real structured controllability/stabilizability/stability radius: Complexity and unified rank-relaxation based methods by Zhang, Yuan, Xia, Yuanqing, Zhan, Yufeng

“…This paper addresses the real structured controllability, stabilizability, and stability radii (RSCR, RSSZR, and RSSR, respectively) of linear systems, which…”
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An incentive mechanism design for mobile crowdsensing with demand uncertainties by Zhan, Yufeng, Xia, Yuanqing, Zhang, Jiang, Li, Ting, Wang, Yu

“…Mobile crowdsensing (MCS) has shown great potential in addressing large-scale data sensing problem by allocating sensing tasks to pervasive mobile users (MU)…”
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Adaptive Federated Learning on Non-IID Data With Resource Constraint by Zhang, Jie, Guo, Song, Qu, Zhihao, Zeng, Deze, Zhan, Yufeng, Liu, Qifeng, Akerkar, Rajendra

“…Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without…”
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