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Minimizing Communication Overhead via Wavelet-Based Model Compression in Federated Learning

Minimizing Communication Overhead via Wavelet-Based Model Co...

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IEEE international conference on Utility and Cloud computing (UCC)

作者： Rómulo W. C. Bustincio Jorge L. Diaz Calle Joahannes B. D. Da Costa Luis F. G. Gonzalez Allan M. de Souza Luiz F. Bittencourt Instituto de Computação (IC) Universidade Estadual de Campinas (UNICAMP) Brazil Departamento de Ciências Básicas ZAB/FZEA Universidade de São Paulo (USP) Brazil Institute of Science and Technology (ICT) Federal University of São Paulo (UNIFESP) Brazil

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Federated Learning (FL) is a decentralized approach for training machine learning models across distributed clients, while maintaining data privacy. Despite its advantages, it faces significant challenges, particularly due to the non-independent and identically distributed (non-IID) nature of the data across clients, which can impact the accuracy of the global model. Furthermore, the participation of numerous clients often results in substantial communication overhead and complex resource management. In this paper, we propose FedWave, a novel method designed to reduce communication overhead without significantly increasing computational costs. Our methodology leverages Harten’s multiresolution analysis to apply wavelet transforms to machine learning models, extracting key coefficients. By transmitting only the most significant coefficients, we achieve efficient model compression, reducing the amount of data communicated between clients and the central server. Unlike Singular Value Decomposition (SVD), commonly used for dimensionality reduction, our method offers computational simplicity and faster compression, making it highly suited for real-time, large-scale FL applications. the results demonstrate that our approach achieves comparable accuracy to SVD-based models while significantly decreasing both communication and computational costs, even in distributed, non-IID data environments.

关键词： Wavelet transforms Training Accuracy Federated learning Computational modeling distributed databases Data models Computational efficiency Servers Singular value decomposition

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LIGHTWEIGHT WORKFLOW ENGINE BASED ON HADOOP AND OSGI

LIGHTWEIGHT WORKFLOW ENGINE BASED ON HADOOP AND OSGI

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2013 5th IEEE international conference on Broadband Network & Multimedia technology

作者： Shengmei LUO Lixia LIU Juan YANG Di ZHANG Department of Computer Science and Technology Tsinghua University

ISBN: (纸本)9781479900961

the growth of data used by data-intensive computations has out-paced the growth of the power of a single processor. In this paper, we propose a high performance and scalability workflow engine, a lightweight parallel and distributed computing platform based on HADOOP and OSGI. Workflow engine provides flexible and scalable OSGI-based interfaces which user can implement to define data processing functions and expand functions in HADOOP ecosystem. Workflow engine consists of invoking interfaces, scheduler engine, optimizer and fault-tolerance manager. Given a workflow process, the engine analyzes data dependencies among nodes, then dispatches them to Map Reduce clusters based on the current status of the system. this paper also describes the design and strategy in detail on implementation processes, key points, relationships of each participants and etc. Our experiment demonstrates that workflow engine can significantly improve the performance of workflow execution.

关键词： Workflow Engine HADOOP OSGI RPC Scheduler Fault-tolerance

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Modeling Neural Representation of Social Recognition Information with Recurrent Neural Networks

Modeling Neural Representation of Social Recognition Informa...

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IEEE/ICME international conference on Complex Medical Engineering, CME

作者： Fei Song Xinnian Wang Yang Zhan Fengzhen Tang Bailu Si State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Division of Life Science and Medicine School of Life Sciences University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Systems Science Beijing Normal University Beijing China Chinese Institute for Brain Research Beijing China

ISBN: (数字)9798350316117

ISBN: (纸本)9798350316124

Social recognition, the ability to differentiate between familiar and unfamiliar individuals, is crucial for animals' survival and reproduction. Neuroscience research has predominantly focused on investigating the brain regions and circuits that regulate social recognition. However, a lingering question pertains to the precise mechanisms underlying the social recognition process. To address this question, we employed a recurrent neural network (RNN) to determine whether an agent can recognize familiar individuals without involving higher-level decision-making processes. this approach takes into consideration that animal behavioral performance may not directly mirror complex cognitive functions. the RNN was trained using the full-FORCE learning paradigm. Our simulation results revealed three distinct subsets of neurons with specific functions: one dedicated to representing novel individuals and two others representing the same social individual in both unfamiliar and familiar states. We propose a hypothesis regarding the potential memory computing processes involved in these operations.

关键词： Analytical models Recurrent neural networks Neuroscience Animals Computational modeling Simulation Neurons

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Application of FBG Sensing technology in Flight Test Strain Measurement

Application of FBG Sensing Technology in Flight Test Strain ...

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IEEE international conference on Power, intelligent computing and Systems (ICPICS)

作者： Yanzhi Li Xiaofeng Jia Lin Li Dongsheng Hu Chengdu Aircraft Corporation Chengdu China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In recent years, there are more and more new aircraft models in the aviation industry, and the structural strain data of aircraft is crucial to the reliability of aircraft, and the ensuing problem is the problem of structural safety monitoring of new aircraft. the traditional test flight test adopts the resistance strain gauge test technology for variable acquisition, which has some unavoidable shortcomings. Fiber grating sensors have the advantages of high sensitivity and precision, strong resistance to electromagnetic interference, corrosion resistance, good inherent safety, small size, etc. their application in aviation field is an important part of improving airborne strain data acquisition technology. the purpose of this paper is to verify the feasibility, effectiveness and reliability of fiber Bragg grating sensor for strain acquisition in flight test, and to lay a foundation for its further application. Firstly, the basic principle of fiber grating sensor is introduced in this paper. Secondly, the common method of temperature compensation of FBG sensor technology is introduced and the cost-effective method is selected according to the actual situation. Finally, compared with the test results of the traditional resistance strain gauge, the purpose of the successful application of FBG fiber optic sensor in airborne strain test is verified through data analysis, and more technical details are proposed to provide reference and valuable field experience and data for the application of more complex and accurate distributed fiber optic stress sensing system in flight test.

关键词： Temperature sensors Resistance Optical fiber sensors Fiber gratings Strain measurement Sensors Reliability Optical fiber testing Aircraft Strain

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A service discovery mechanism with load balance issue in decentralized peer-to-peer network

A service discovery mechanism with load balance issue in dec...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Ching-Wen Chen Phui-Si Gan Chao-Hsiang Yang Department of Computer Science and Information Engineering Chaoyang University of Technology Taiwan

Peer-to-peer systems (P2P) have attracted much current attention as a popular way to share huge volumes of resources directly within peers. Efficient content search and load balancing are both important research issues in P2P environments, especially in those without centralized global indexes. Although a number of content search schemes are known to alleviate these problems, they cannot provide flexible object searches while balancing the load for content demand in P2P networks, thereby resulting in severe load imbalance and consequently increased user response times. In this paper, we tend to address these problems that take advantage of current domain name server (DNS) architecture by harnessing all available resources in the P2P networks and balance the load without decreasing user response time. We proposed a novel decentralized service discovery mechanism by creating an agent called P2P registry (P2PReg) as a middleware within DNS and peers. In our scheme, each peer is able to register and discover desirable services automatically through current DNS within a short duration in an efficient way with the help of P2PReg. the proposed method does not only perform least searching time and low bandwidth but also balances load in P2P systems in order to avoid hot spot (refers to a node which receives a large volume of requests in rapidly) problems naturally. It is obvious that the more replicas there are, the better the media delivery quality of the specific content object is. We also present the results of simulation work to validate the viability of our approach. Our simulation study showed that serving data discovery over local search is indeed effective in improving the system performance significantly.

关键词： intelligent networks Peer to peer computing Network servers Load management Chaos Delay distributed computing Web server Gallium nitride Computer science

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A Class of Fast and Accurate Multi-layer Block Summation and Dot Product Algorithms 18th

A Class of Fast and Accurate Multi-layer Block Summation a...

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18th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2021

作者： He, Kang Barrio, Roberto Chen, Lin Jiang, Hao Liu, Jie Gu, Tongxiang Qi, Jin Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Department of Applied Mathematics University of Zaragoza ZaragozaE50009 Spain College of Computer National University of Defense Technology Changsha410073 China Institute of Applied Physics and Computational Mathematics Beijing100000 China

ISBN: (纸本)9783030935702

Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algorithms respectively called FABsum and FABdot, which trades off the calculation accuracy and speed by the block size. Castaldo [2] proposed a multi-layer block summation and dot product algorithm called SuperBlocksum and SuperBlockdot that can increase the accuracy while adding almost no additional calculations. We combine the idea of [1] with the multi-layer block structure to propose SuperFABsum (for "super fast and accurate block summation") and SuperFABdot (for "super fast and accurate block dot product"). Our algorithms have two variants, one is SuperFAB(within), the other is SuperFAB(outside). Our algorithms further improve accuracy and speed compared with FAB and SuperBlock. We conducted accuracy and speed tests on the high-performance FT2000+ processor. Experimental results show that SuperFABdot(within) algorithm is more accurate than FABdot and SuperBlockdot. Compared with FABdot, SuperFABdot(outside) algorithm can achieve up to 1.2 × performance speedup while ensuring similar accuracy. © 2022, IFIP international Federation for Information Processing.

关键词： Artificial intelligence

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Mobile ambient media: between human, art, and technology 08

Mobile ambient media: between human, art, and technology

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Proceedings of the 6th international conference on Advances in Mobile computing and Multimedia

作者： Artur Lugmayr Tampere University of Technology Tampere Finland

ISBN: (纸本)9781605582696

the medium is the message -- and the medium is more and more distributed throughout the natural environment of the consumer. Smart mobile phones, location based services, smart fridges, intelligent homes, and objects that think are just a few examples. these form of media is called ambient media -- media transmitting information and knowledge in our daily living environments. this keynote addresses emerging service types and covers mobile ambient media from the human, artistic, and technology viewpoint. Latest trends coming from social media, pervasive mobile computation, ubiquitous communication, and technological enabler are presented. Another question addressed is how emerging content forms might look like. After the more visionary viewpoint, down-to-earth business cases of successful ambient media services are presented. However, the future is 'ambient'.

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Regenerative Braking Energy Utilization by Multi Train Cooperation

Regenerative Braking Energy Utilization by Multi Train Coope...

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17th international IEEE conference on intelligent Transportation Systems & the Asia-Pacific Council on Systems Engineering conference 2014

作者： Xubin Sun Hu Cai Xiaowei Hou Mengyang Zhang Hairong Dong School of Electronic and Information Engineering Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University

Regenerative braking system is widely used on subway trains,which will transmit kinetic energy of the trains to *** the braking speed of a train is comparatively high,regenerative braking is prior to the mechanical ***,if the regenerative braking energy can not be absorbed by other trains in the same power supply section,the regenerative braking energy may lead to the voltage rising,even have to use dissipative resistance to absorb the surplus *** expected situation is that the regenerative braking energy is absorbed by other trains in the same power supply section as much as ***-train cooperation method is given in this paper,where the speed profile of the trains,selected to absorb the regenerative braking energy,will be partly ***,part of the original speed profile will be replaced by coast-accelerate-coast strategy,the objective is to make the train run as far as possible by only using the distributed regenerative energy.A case is studied based on Beijing Yizhuang Subway line,where speed profiles of two trains are adjusted to absorb the regenerative braking energy generated by a braking train at the same power supply section.

关键词： multi-train cooperation,regenerative braking energy,train speed profile

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Management and Maintenance of Buildings in BIM Environment: Facility Management of Historic Buildings (HBIM) 9th

Management and Maintenance of Buildings in BIM Environment: ...

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9th international conference on Mobility, IoT and Smart Cities, EAI Mobility IoT 2022

作者： Mésároš, Peter Grazianová, Mária Smetanková, Jana Krajníková, Katarína Behúnová, Annamária Technical University of Kosice Faculty of Civil Engineering Institute of Construction Technology Economics and Management Kosice Slovakia Technical University of Kosice Faculty of Civil Engineering Expert’s Institute in Construction Kosice Slovakia National Infrastructure for Technology Transfer Support in Slovakia II – NITT SK II Bratislava Slovakia Technical University of Košice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Kosice Slovakia

ISBN: (纸本)9783031282249

To ensure the longest and most efficient operation of the building, it is necessary to use the necessary construction equipment correctly and to detect and eliminate all errors and deficiencies in a timely manner. Maintenance of building represents a combination of technical, administrative and managerial activities during the operation of buildings, the priority objective of which is to maintain the required function. Innovative methods and tools supporting the facility management of buildings are increasingly being used in the management and maintenance of buildings. Such innovative technologies also include building information modelling (BIM). BIM provides users with important information and data that help simplify planning, implementation and maintenance of buildings throughout their life cycle. Precisely in the management and maintenance of historical buildings, sufficient data and information are key aspects to eliminate the occurrence of many faults and damage to historical buildings of the monument fund, which is caused by simple neglect of care or inappropriate maintenance. For this purpose, the principles of Heritage Building Information Modelling (HBIM) are increasingly applied in construction practice. As part of the research, will be analysed the current situation in the field of facility management of historic buildings and the current level of knowledge of the term HBIM. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Maintenance

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Performance Optimization Blockchain-Enabled Fog computing with Deep Reinforcement Learning 22

Performance Optimization Blockchain-Enabled Fog Computing wi...

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Proceedings of the 2022 12th international conference on Communication and Network Security

作者： Yihe Zhang Shanshan Tu Shengju Zhang Yongjie Yang Aiming Wu Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China China Mobile (Suzhou) Software Technology Co. Ltd China Mobile (Suzhou) Software Technology Co. Ltd China

ISBN: (纸本)9781450397520

With the rapid development of Internet of things technology and cloud computing technology, Fog computing emerges with the demand of distributed and decentralized. Blockchain is considered to be an advanced technology, which enables Fog computing data to be stored and shared safely, greatly improving the security and privacy of Fog computing. However, the Blockchain technology currently applied in Fog computing still has some significant disadvantages, one of which is the low throughput performance. In order to meet the demand of improving throughput, this paper proposes a Blockchain-Enabled Fog computing performance optimization framework based on Deep Reinforcement Learning (DRL), which can dynamically adjust according to the system state to improve throughput. Dueling Deep Reinforcement Learning is adopted to obtain the optimal configuration, which can be dynamically adjusted according to Fog node status, network bandwidth and user's quality of service (QoS) requirements, including dynamic selection of block producers, block size and network bandwidth allocation. Simulation results show that the proposed scheme can effectively improve throughput performance.

关键词： Blockchain quality of service Deep Reinforcement Learning Fog computing

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