检索结果-内蒙古大学图书馆

Learn to Collaborate in MEC: An Adaptive Decentralized Federated Learning Framework

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14071-14084页

作者： Wang, Yatong Wen, Zhongyi Li, Yunjie Cao, Bin Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.

关键词： Collaboration Training Computational modeling Computer architecture Federated learning Servers distributed databases mobile edge computing option-critic multi-agent reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

GridAPPS-D distributed App Architecture and API for Modular and distributed Grid Operations

引用

IEEE ACCESS 2024年 12卷 39862-39875页

作者： Sharma, Poorva Reiman, Andrew P. Anderson, Alexander A. Poudel, Shiva Allwardt, Craig H. Fisher, Andrew R. Slay, Tylor E. Mukherjee, Monish Dubey, Anamika Ogle, James P. Melton, Ronald B. Pacific Northwest Natl Lab Richland WA 99354 USA Washington State Univ Dept Elect Engn & Comp Sci Pullman WA 99163 USA

As power distribution systems evolve in complexity and scale, the coordination and control of distributed energy resources (DERs), intelligent devices, and agents become increasingly challenging. Distribution utilities invest in advanced distribution management systems (ADMSs) and distributed energy resource management systems (DERMSs) to enhance distribution systems' reliability, resiliency, and efficiency. Standardizing the Application Programming Interface (API) for ADMS applications is crucial to accelerate the integration of advanced distribution technologies. This paper introduces a distributed application architecture within the GridAPPS-D platform, aiming to address the limitations of centralized architectures in terms of scalability, maintainability, and flexibility. The proposed architecture draws inspiration from the Laminar Coordination Framework and is validated through extensive stakeholder engagement. Emphasizing extensibility, boundary deference, structural scalability, and securability, the layered framework is well-suited for large-scale distribution networks with diverse grid-edge devices and ownership structures. The contributions of this paper include a distributed layered architecture with defined distributed areas, a Common Information Model (CIM)-based standardized API for developing and deploying distributed applications (distributed App API), and the design process and reference implementation of distributed services and applications. Based on laminar coordination, the software architecture combines centralized, distributed, and edge-control paradigms for effective distributed operations. The paper concludes with extending the centralized API in GridAPPS-D to distributed APIs for standards-based message exchange, emphasizing the need for scalable communication to coordinate diverse distributed agents. This work provides a foundation for advancing the field of distributed control in power distribution systems, supporting both centralized and

关键词： Computer architecture Application programming interfaces distributed power generation Software Scalability distributed databases Software architecture Power distribution Reliability engineering Application programming interface distributed applications distribution management systems software architecture

来源：评论

学校读者我要写书评

暂无评论

distributed Indexing Schemes for K-Dominant Skyline Analytics on Uncertain Edge-IoT Data

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第3期12卷 878-890页

作者： Lai, Chuan-Chi Lin, Hsuan-Yu Liu, Chuan-Ming Feng Chia Univ Dept Informat Engn & Comp Sci Taichung 407102 Taiwan Taiwan Semicond Mfg Co Ltd TSMC Tool Prod Optimizat Dept Hsinchu Taiwan Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei Taiwan

Skyline queries typically search a Pareto-optimal set from a given data set to solve the corresponding multiobjective optimization problem. As the number of criteria increases, the skyline presumes excessive data items, which yield a meaningless result. To address this curse of dimensionality, we proposed a k-dominant skyline in which the number of skyline members was reduced by relaxing the restriction on the number of dimensions, considering the uncertainty of data. Specifically, each data item was associated with a probability of appearance, which represented the probability of becoming a member of the k-dominant skyline. As data items appear continuously in data streams, the corresponding k-dominant skyline may vary with time. Therefore, an effective and rapid mechanism of updating the k-dominant skyline becomes crucial. Herein, we proposed two time-efficient schemes, Middle Indexing (MI) and All Indexing (AI), for k-dominant skyline in distributed edge-computing environments, where irrelevant data items can be effectively excluded from the compute to reduce the processing duration. Furthermore, the proposed schemes were validated with extensive experimental simulations. The experimental results demonstrated that the proposed MI and AI schemes reduced the computation time by approximately 13% and 56%, respectively, compared with the existing method.

关键词： Streams Indexing Artificial intelligence Probabilistic logic Optimization distributed databases Uncertainty k-dominant skyline data streams pareto-optimal skyline uncertain data

来源：评论

学校读者我要写书评

暂无评论

Stopping Criteria for distributed Data Storage in Compressive CrowdSensing Systems

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第7期11卷 11767-11778页

作者： Liu, Xingting Zhou, Siwang Peng, Jiaxin Zhang, Wei Tang, Deyan Li, Keqin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Changsha Univ Sch Comp Sci & Engn Changsha 410022 Peoples R China Hunan First Normal Univ Sch Comp Sci Changsha 410205 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

distributed data storage (DDS) in mobile crowdsensing (MCS) systems has recently gained popularity. Data should be briefly saved on participants' mobile devices before being gathered once the centralized cloud servers resume normal operations. For MCS systems, the existing DDS strategies briefly considered reconstructing the scene as precisely as possible without thinking about the costs of each step. However, our goal is to obtain a sufficiently accurate approximation of the sensing data from mobile participants with as few costs as possible. We note a crucial observation: when a specified number of participants have been transmitted to a central server, the sensing data has already been well reconstructed, and the accuracy advancement with additional transmitted participants is minimal. In our scheme, two stopping criteria are proposed for DDS in compressive MCS, which aims to enhance recovery performance while reducing the costs of the whole process. In the first stopping criterion, we established a rule to stop the continued recruitment of participants. The algorithm adaptively increases the number of participants until the reconstruction accuracy meets the requirement. Another stopping criterion of the reconstruction algorithm is designed to find a more accurate number of iterations than the original. The experiment results demonstrate that the first stopping criterion can reduce participants' collection while obtaining an approximate value. The second stopping criterion assists the reconstruction algorithm in terminating at a more appropriate number of iterations, saving computing costs while ensuring accuracy.

关键词： Sensors Costs Servers Memory Crowdsensing Cloud computing distributed databases Adaptive participant compressive sensing (CS) crowdsensing distributed storage stopping rules

来源：评论

学校读者我要写书评

暂无评论

Bayesian Data Fusion With Shared Priors

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 275-288页

作者： Wu, Peng Imbiriba, Tales Elvira, Victor Closas, Pau Northeastern Univ Elect & Comp Engn Dept Boston MA 02115 USA Univ Edinburgh Sch Math Edinburgh EH8 9JU Scotland

The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential. In Bayesian settings, a priori information of the unknown quantities is available and, possibly, present among the different distributed estimators. When the local estimates are fused, the prior knowledge used to construct several local posteriors might be overused unless the fusion node accounts for this and corrects it. In this paper, we analyze the effects of shared priors in Bayesian data fusion contexts. Depending on different common fusion rules, our analysis helps to understand the performance behavior as a function of the number of collaborative agents and as a consequence of different types of priors. The analysis is performed by using two divergences which are common in Bayesian inference, and the generality of the results allows to analyze very generic distributions. These theoretical results are corroborated through experiments in a variety of estimation and classification problems, including linear and nonlinear models, and federated learning schemes.

关键词： Bayes methods Data integration distributed databases Federated learning Signal processing Estimation Distance learning Data fusion Bayesian inference distributed machine learning federated learning

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Product-Driven Manufacturing System Using Data Distribution Service

引用

IEEE ACCESS 2024年 12卷 16447-16461页

作者： Lin, Chin-Te Lu, Hong-Ji Natl Cent Univ Dept Mech Engn Taoyuan 32001 Taiwan

As key components of smart factories, intelligent production machines are gradually becoming a major capital asset of companies in the market, offering connectivity, interoperability, and services. However, due to commercial and technological constraints, production machines rarely provide enough information for customers to develop data-driven applications. Therefore, this research proposes that intelligence be built into them to assist in production control and allow relevant data to be collected and made available for future use. In order to realize this proposal, an operational paradigm for such systems is first outlined in this paper. Then, the states of workpieces in a production environment are classified, and the methods of generation, operation, and transfer of instances are described. The next step is to build an intelligent product-driven manufacturing system using Data Distribution Service (DDS). Intelligent workpieces and machines collaborate to complete production tasks in such a system. During this collaborative process, an intelligent workpiece continuously collects relevant data and packages it together for the customer on its transformation into a final product. The feasibility of this intelligent product-driven manufacturing system is examined by upgrading a classic manufacturing factory. In addition, the study explores the system architecture, the implementation of a classic system upgrade, the production data collection, and the intellectualization of data assets. In conclusion, the results of this study will help machine builders expand their business models, providing customers with production machines that carry data for advanced data-driven applications.

关键词： Manufacturing systems Production facilities Fourth Industrial Revolution distributed databases Data processing Information exchange Product lifecycle management Data distribution service intelligent product manufacturing system product life cycle industry 4.0 industrial automation

来源：评论

学校读者我要写书评

暂无评论

Joint Dataset Reconstruction and Power Control for distributed Training in D2D Edge Network

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 132-147页

作者： Wu, Jiaxin Wu, Jigang Chen, Long Sun, Yifei Wu, Yalan Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device's dataset into independent and identically distributed dataset via data exchange among trusted devices. For energy efficiency, we further consider power control for the devices. We then formulate an optimization problem, which is a mixed integer non-linear programming problem, to minimize the total energy consumption for each round of distributed training. Due to the NP-hardness and coupling property of the optimization problem, we decompose it into two subproblems for dataset reconstruction and power control, respectively. An approximation algorithm is designed to obtain a near-optimal auxiliary devices set for dataset reconstruction with minimum energy consumption, while meeting the variance constraint of the optimization problem. We prove that approximation algorithm has a worst-case approximation ratio of 1+ln|Omega i(t)|, where |Omega i(t)| is the required data samples for dataset reconstruction of each training device. For power control, we design a dynamic programming algorithm to further reduce the energy consumption. For comparison, we propose three benchmark schemes that adopt either one of the algorithms or neither. We also customize three baseline algorithms based on the state-of-the-arts to compare with our proposed algorithm. Numerical results show that, our proposed algorithm outperforms three benchmarks on the average energy consumption for one round for different cases. When varying the labels that each device owns, our proposed algorithm outperforms the other three baseline algorithms on training accuracy. Besides, when setting a target accuracy, our proposed algorithm always has the lowest energy consumption.

关键词： Training distributed databases Device-to-device communication Data models Approximation algorithms Adaptation models Servers D2D distributed training NP-hard weighted set cover approximation algorithm dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Data-Driven distributed Robust Monitoring and Control Optimization for Interconnected Systems

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1399-1408页

作者： Wang, Hao Luo, Hao Qiao, Xinyu Huo, Mingyi Xu, Xiaoyi Harbin Inst Technol Dept Control & Simulat Ctr Harbin 150001 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

This article proposes a projection-aided robust distributed monitoring and control optimization approach for interconnected systems with disturbances. The disturbances and state coupling between subsystems are a challenge in achieving accurate distributed process monitoring using data-driven techniques. To address the problems, a distributed adaptive residual generator uses the average consensus algorithm to perform data fusion on the subsystem residual generator to implement disturbance decoupling process monitoring. The key to implementing this process is to use input and output data disturbance in the perturbed orthogonal complementary space to drive the adaptive residual generator. Then, using the projection technique, the residual signal in the disturbance space drives the distributed learning of plug-and-play (PnP) controller parameters. The average consensus algorithm ensures that the subsystem PnP controller parameter gradient consistency converges to the centralized design. The feasibility and effectiveness of the proposed approach are verified and demonstrated through a simulation.

关键词： Monitoring Interconnected systems Generators Optimization Observers Aerospace electronics Accuracy distributed databases Computational modeling Principal component analysis Data-driven (DD) distributed monitoring disturbance rejection control interconnected systems

来源：评论

学校读者我要写书评

暂无评论

DeFedTL: A Decentralized Federated Transfer Learning Method for Fault Diagnosis

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1704-1713页

作者： Xu, Danya Liu, Yi Wen, Guanghui Jin, Yaochu Chai, Tianyou Yang, Tao Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310014 Peoples R China Southeast Univ Dept Syst Sci Nanjing 211102 Peoples R China Westlake Univ Sch Engn Hangzhou 310024 Peoples R China

Deep learning has become increasingly important in fault diagnosis, but it relies on a large amount of high-quality labeled data. Collecting data from distributed machines can expand the dataset, but it usually leads to privacy concerns. Moreover, since the operating conditions are complex in real-world applications, the collected training data and the test data often have different distributions. Therefore, a well-trained model on the training data may not be suitable for test data due to the domain shift. To preserve privacy and to mitigate the domain shift, in existing federated transfer learning fault diagnosis methods, distributed machines exchange model parameters and features rather than raw data with the central server. However, such methods suffer from a single point of failure and high communication burden. To address these issues, we propose a fully decentralized federated transfer learning fault diagnosis method. More specifically, the proposed method obtains a pretrained model among source nodes with labeled training data where each source node exchanges model parameters with its neighboring source nodes. Moreover, a novel transfer learning strategy is proposed, which aligns features of test data at the target node with features of training data at its connected source nodes to mitigate misclassifications resulting from the domain shift. The effectiveness of the proposed method is verified by various experiments on two public bearing datasets.

关键词： Fault diagnosis Servers Data models Transfer learning distributed databases Training Optimization Informatics Training data Federated learning Domain shift fault diagnosis federated learning (FL) privacy preservation transfer learning (TL)

来源：评论

学校读者我要写书评

暂无评论

Adaptive Encoder-Decoder Model Considering Spatio-Temporal Features for Short-Term Power Prediction of distributed Photovoltaic Station

引用

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1363-1373页

作者： Dou, Xun Deng, Yehang Wang, Shunjiang Chu, Tianfeng Li, Jiacheng Luo, Haifeng Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China State Grid Liaoning Elect Power Co Ltd Shenyang 110006 Peoples R China

Considering the impact of operation and maintenance costs and technology, there is generally a lack of sufficient meteorological observation devices within the distributed photovoltaic (PV) station group. The deviation of the collected meteorological data and the PV power data error caused by software and hardware limitations will directly lead to the reduction of model prediction accuracy. To tackle this problem, this article proposes a short-term prediction method with adaptive spatio-temporal codec structure for distributed PV power prediction, which adapts to the prediction requirements of different data input and different weather conditions and improves the prediction accuracy. First, the Random Forest algorithm (RF) and Pearson Correlation Coefficient (PCC) are used to sort the feature importance and select the key input data. Second, a spatio-temporal feature encoder-decoder model based on Long Short Term Memory Network (LSTM) and Spatio-Temporal Attention mechanism (STA) is proposed to adapt to spatio-temporal feature mining under different weather conditions. Third, an adaptive prediction framework based on pre-fusion and post-fusion is designed to meet the needs of comprehensive feature learning under the input of different amounts of data and further improve the prediction accuracy. Comprehensive experiments have been conducted on real data from different stations in Jiangsu, China, to confirm the superior performance of the proposed model.

关键词： Predictive models Data models distributed databases Adaptation models Feature extraction Photovoltaic systems Software Adaptive encoder-decoder model adaptive prediction framework distributed PV station short-term power prediction spatio-temporal features

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：