The Experts below are selected from a list of 25044 Experts worldwide ranked by ideXlab platform
Yingjun Angela Zhang - One of the best experts on this subject based on the ideXlab platform.
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reconfigurable intelligent surface enabled federated learning a Unified Communication learning design approach
IEEE Transactions on Wireless Communications, 2021Co-Authors: Hang Liu, Xiaojun Yuan, Yingjun Angela ZhangAbstract:To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high Communication latency and privacy issues as compared to centralized ML. To improve the Communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of Communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model Communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a Unified Communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.
Hang Liu - One of the best experts on this subject based on the ideXlab platform.
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reconfigurable intelligent surface enabled federated learning a Unified Communication learning design approach
IEEE Transactions on Wireless Communications, 2021Co-Authors: Hang Liu, Xiaojun Yuan, Yingjun Angela ZhangAbstract:To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high Communication latency and privacy issues as compared to centralized ML. To improve the Communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of Communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model Communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a Unified Communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.
Adnan M Abumahfouz - One of the best experts on this subject based on the ideXlab platform.
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cognitive radio based sensor network in smart grid architectures applications and Communication technologies
IEEE Access, 2017Co-Authors: Emmanuel U Ogbodo, David G Dorrell, Adnan M AbumahfouzAbstract:The cognitive radio-based sensor network (CRSN) is envisioned as a strong driver in the development of modern power system smart grids (SGs). This can address the spectrum limitation in the sensor nodes due to interference cause by other wireless devices operating on the same unlicensed frequency in the Industrial, Scientific and Medical band. These sensor nodes are used for monitoring and control purposes in various components of a SG, ranging from generation, transmission, and distribution, and down to the consumer, including monitoring of utility network assets. A reliable SG Communication network architecture is required for transferring information which needed by the SG applications, alongside the monitoring and control by CRSN. Hence, this paper investigates and explores the CRSN conceptual framework, and SG Communication architecture with its applications; vis-a-vis the Communication access technologies, including implementation design with quality of service support. Consequently, this paper highlights various research gaps, such as implementation design model, utilization of LPWAN for CRSN based SG deployment, and so on. This includes discussion on the future direction for various aspects of the CRSN in SG. To address these research gaps, we introduced a smart Unified Communication solution to improve the efficiency of the SG and mitigate various associated challenges.
Xiaojun Yuan - One of the best experts on this subject based on the ideXlab platform.
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reconfigurable intelligent surface enabled federated learning a Unified Communication learning design approach
IEEE Transactions on Wireless Communications, 2021Co-Authors: Hang Liu, Xiaojun Yuan, Yingjun Angela ZhangAbstract:To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high Communication latency and privacy issues as compared to centralized ML. To improve the Communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of Communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model Communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a Unified Communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.
Zhang, Ying-jun Angela - One of the best experts on this subject based on the ideXlab platform.
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Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
2021Co-Authors: Liu Hang, Yuan Xiaojun, Zhang, Ying-jun AngelaAbstract:To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high Communication latency and privacy issues as compared to centralized ML. To improve the Communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of Communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model Communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a Unified Communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
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Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
2021Co-Authors: Liu Hang, Yuan Xiaojun, Zhang, Ying-jun AngelaAbstract:To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high Communication latency and privacy issues as compared to centralized ML. To improve the Communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of Communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model Communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a Unified Communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.Comment: Simulation codes are available at https://github.com/liuhang1994/RIS-FL. This work has been accepted by IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessibl
