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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models 22nd

Federated Learning as a Service for Hierarchical Edge Networ...

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22nd international conference on Service Oriented computing

作者： Gao, Wentao Tavallaie, Omid Chen, Shuaijun Zomaya, Albert Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9789819608041;9789819608058

Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (FLaaS) offers a privacy-preserving approach for training machine learning models on devices with various computational resources. Most proposed FL-based methods train the same model in all client devices regardless of their computational resources. However, in practical Internet of Things (IoT) scenarios, IoT devices with limited computational resources may not be capable of training models that client devices with greater hardware performance hosted. Most of the existing FL frameworks that aim to solve the problem of aggregating heterogeneous models are designed for Independent and Identical distributed (IID) data, which may make it hard to reach the target algorithm performance when encountering non-IID scenarios. To address these problems in hierarchical networks, in this paper, we propose a heterogeneous aggregation framework for hierarchical edge systems called HAF-Edge. In our proposed framework, we introduce a communication-efficient model aggregation method designed for FL systems with two-level model aggregations running at the edge and cloud levels. This approach enhances the convergence rate of the global model by leveraging selective knowledge transfer during the aggregation of heterogeneous models. To the best of our knowledge, this work is pioneering in addressing the problem of aggregating heterogeneous models within hierarchical FL systems spanning IoT, edge, and cloud environments. We conducted extensive experiments to validate the performance of our proposed method. The evaluation results demonstrate that HAF-Edge significantly outperforms state-of-the-art methods.

关键词： Federated Learning as a Service (FLaaS) Model Heterogeneity Edge computing IoT Networks

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Demo: Proof-of-Work Network Simulator for Blockchain and Cryptocurrency Research 41

Demo: Proof-of-Work Network Simulator for Blockchain and Cry...

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41st ieee international conference on distributed computing systems (ICDCS)

作者： Wuthier, Simeon Chang, Sang-Yoon Univ Colorado Colorado Springs CO 80907 USA

ISBN: (纸本)9781665445139

Blockchain and the proof-of-work (PoW) distributed consensus protocol rely on peer-to-peer (P2P) networking. We build a PoW P2P simulator for the modeling and analyses of permissionless blockchain networking. Our simulator utilizes a built-in randomness generator for the simulations, has an easy-to-use interface and intuitive visualization, supports dynamic/programmable control and modifications, and can generate simulation data for further processing. We publish our simulator in open source to facilitate its use for blockchain and P2P networking research and especially recommend it for scalability research or preliminary testing. To highlight its features and capabilities, we demonstrate the simulator use in this paper to analyze the recent blockchain security research, including 51% attack, eclipse, partitioning, and DoS attack.

关键词： Scalability Process control Data visualization Data models Generators Blockchains Peer-to-peer computing

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Design of a computing Power Token Trading Platform for Government Applications

Design of a Computing Power Token Trading Platform for Gover...

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2024 international conference on Culture-Oriented Science and Technology, CoST 2024

作者： Jiang, Ziyu Communication University of China Beijing China

ISBN: (纸本)9798350380347

This paper deeply explores the design of computing power volume syndrome trading platform applicable to the government, which aims to optimize the distribution and management of computing power resources through blockchain technology. The paper overview of the core role of computing power in the era of wisdom, analyzing the current status and challenges of the Chinese computing market, including the rapid growth of computing power demand, the imbalance of resource distribution, and the problem of supply. computing power management emphasizes the potential of blockchain technology in terms of improving transaction security, reducing costs, and improving resource management efficiency. The paper details the key factors of the concept, role, goals, and effective role of computing coupons. In addition, the design framework of the computing power coupon communication platform, including character definition, core function, operating logic, pricing strategy, transaction process and settlement mechanism, and discuss the application of consensus mechanisms in the platform, especially the rights certificate mechanism (POS), And propose a variety of application scenarios of smart contracts in the platform. This paper provides comprehensive guidance and reference for the government and other relevant institutions strongly supported by the government to design the computing power token trading platform. © 2024 ieee.

关键词： Power systems computer aided design

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Comparative Analysis of Cloud computing and the Gig Economy Pricing Strategies

Comparative Analysis of Cloud Computing and the Gig Economy ...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jothish Kumar, M. Reddy, Gajulapalli Venkata Karthik Kumar Iyapparaja Nowritha, S. School of Computer Science Engineering and Information Systems Vit Vellore Department of Smart Computing Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vit Vellore Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vit University Department of Smart Computing Tamil Nadu Vellore India

ISBN: (纸本)9798331543617

Cloud computing services and gig economy platforms vary in flexibility, efficiency, and scalability due to their pricing schemes. Cloud computing services provide flexibility and cost control via pay-as-you-go, subscription, and freemium models. Like gig economy platforms, workers and customers pay for task completion, time-based contracts, and premium services. Assessing how successfully different pricing techniques fulfil varied demands, from short-term initiatives to long-term commitments, requires identifying their strengths and weaknesses. The goal is to evaluate these models' capacity to meet shifting demand, save money, and scale. The methodology also assesses cost predictability and company and consumer advantages of each pricing structure. The comparison seeks to improve resource allocation and financial planning by revealing how various pricing systems function in different circumstances. The research helps decision-makers use cloud services or gig economy platforms for different operational purposes by emphasizing their economic consequences. From datasets, simulated pricing model data was examined. The first example includes settings such as Hours Used (5-25), Pay-as-You-Go (10-50 USD), and Subscription-Based (30 USD). The second instance has Job Size (Resources) from 5 to 25, Freemium Model (USD) from 10 to 30, and Usage-Based Pricing (USD) from 10 to 50. © 2024 ieee.

关键词： Cloud platforms

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Querying distributed Sensor Streams in the Edge-to-Cloud Continuum 6

Querying Distributed Sensor Streams in the Edge-to-Cloud Con...

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6th ieee international conference on Edge computing and Communications (ieee EDGE)

作者： Karlstetter, Roman Widhopf-Fenk, Robert Schulz, Martin IfTA GmbH Puchheim Germany Tech Univ Munich Garching Germany

ISBN: (数字)9781665481403

ISBN: (纸本)9781665481403

Sensor data is of crucial importance in many IoT scenarios. It is used for online monitoring as well as long term data analytics, enabling countless use cases from damage prevention to predictive maintenance. Multivariate sensor time series data is acquired and initially stored close to the sensor, at the edge. It is also beneficial to summarize this data in windowed aggregations at different resolutions. A subset of the resulting aggregation hierarchy is typically sent to a cloud infrastructure, often via intermittent or low bandwidth connections. Consequently, different views on the data exist on different nodes in the edge-to-cloud continuum. However, when querying this data, users are interested in a fast response and a complete, unified view on the data, regardless of which part in the infrastructure continuum they send the query to and where the data is physically stored. In this paper, we present a loosely coupled approach that enables fast range queries on a distributed and hierarchical sensor database. Our system only assumes the possibility of fast local range queries on a hierarchical sensor database. It does not require any shared state between nodes and thus degrades gracefully in case certain parts of the hierarchy are unreachable. We show that our system is suitable for driving interactive data exploration sessions on terabytes of data while unifying the different views on the data. Thus, our system can improve the data analysis experience in many geo-distributed scenarios.

关键词： edge cloud multivariate sensor data stream distributed query

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Delay-Aware and Energy-Efficient Task Offloading Based on Adaptive Large Neighborhood Search

Delay-Aware and Energy-Efficient Task Offloading Based on Ad...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Jiang, Mingzhong Lu, Anbang Zhu, Qinghua Fei, Lunke School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350337020

Mobile edge computing boosts the application performance on mobile devices by collaborating with cloud platforms. This paper studies the task offloading and computing resource allocation problem in a multibase, multiserver, and multiuser scenario subject to resource constraints. The goal is to maximize the users' task offloading utility, including improvements in task completion time, energy consumption, and communication cost. The addressed problem is formulated as a mixed integer nonlinear programming (MINLP) model. In this paper, we decompose the MINLP and the optimal computing resource allocation policy under a deterministic offloading strategy obtained by the Karush-Kuhn-Tucker conditions. Then, a hybrid adaptive large neighborhood search (HALNS) algorithm is proposed to conduct task offloading. The adaptive large neighborhood search and the variable neighborhood descent stages are jointly employed in HALNS. The proposed algorithm, an improved simulated annealing algorithm, and a modified variable neighborhood search algorithm are executed to evaluate their performances. Digital experimental results show that our proposed algorithm achieves higher system utility, lower delays, and less energy consumption. © 2023 ieee.

关键词： Mobile edge computing Neighborhood Search Task offloading

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Balancing Crop Priorities: PSO-Based Equitable Irrigation Water Distribution for Efficient Resource Management 17

Balancing Crop Priorities: PSO-Based Equitable Irrigation Wa...

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Ikidid, Abdelouafi Fazziki, Abdelaziz El Baba, Naima Cadi Ayyad University Computer Science Dept Computing Systems Engineering Laboratory Marrakesh Morocco

ISBN: (纸本)9798350338904

Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding losses. This global issue has prompted many scientists to think about sustainable solutions using innovative technologies. This study aims to solve the problem of equitable distribution of irrigation water to crops using the PSO (Particle Swarm Optimization) algorithm. The problem involves the allocation of water between N crops, taking into account the priorities of each crop, the minimum and maximum water thresholds required, and the total amount of water available. A precise modeling of the problem is proposed, followed by a description of the PSO algorithm adapted to this situation. The implementation of the algorithm in Python is also presented. The results and performance of the algorithm are discussed, showing its ability to find a fair and efficient solution for irrigation water allocation. © 2023 ieee.

关键词： Machine learning

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Improved the Accuracy of Indoor Positioning at the Meter Level in Large and Complex Environments Through AI-Powered Sensor Fusion

Improved the Accuracy of Indoor Positioning at the Meter Lev...

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2024 ieee international conference on computing, ICOCO 2024

作者： Asaduzzaman, Mina Tan, Kim Geok Tan, Choo Peng Sayeed, Md. Shohel Hossain, Ferdous Hasan, Md. Abu Sayed Mahfuz Faculty of InformationScience and Technology Multimedia University Melaka Malaysia Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798331530303

Indoor positioning systems (IPS) continue to encounter significant challenges in achieving meter-level accuracy, particularly in large and intricate environments such as airports, hospitals, and industrial sites. Despite substantial advancements in technologies like Wi-Fi Fine Time Measurement (FTM), Ultra-Wideband (UWB), and 5G, their scalability and precision often suffer in dynamic, obstacle-laden settings. To enhance accuracy and adaptability, this study presents an innovative AI-driven sensor fusion technique that integrates machine learning models with various positioning technologies. This study achieves significant improvements in positioning accuracy through dynamic sensor input adjustments and real-time environmental awareness. Additionally, this study also includes results from a thorough evaluation of the system's effectiveness across diverse practical applications, while exploring its potential uses within the Internet of Things (IoT) framework. ©2024 ieee.

关键词： Time measurement

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Quantum computing - Navigating the Frontier with Shor's Algorithm and Quantum Cryptography 2

Quantum Computing - Navigating the Frontier with Shor's Algo...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Thamilarasi, V. Naik, Pramod Kumar Sharma, Isha Porkodi, V. Sivaram, M. Lawanyashri, M. Department of Computer Science Tamilnadu Salem-16 India Dayananda Sagar University Department of Cse Bengaluru India Central University of Haryana Deptt. of Mathematics Haryana India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Score Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350384277

This abstract discusses the transformational capacity of Shor's Algorithm and quantum cryptography in quantum computing. However, Shor's Algorithm poses a problem to classical cryptographic systems such as RSA encryption because of its apparent capacity to multiply speed the integer factorization. This approach relies on multimillions qubit quantum computers with systematic error correction which have yet to be implemented. Quantum cryptography revolutionizes secure communication via Quantum Key Distribution (QKD). It uses quantum mechanics to design the unbreakable key exchange system that can identify eavesdropping. Despite the prospect, quantum cryptography is hampered by limits in distances and speeds of QKD transmission. Quantum repeaters and satellite-based QKD make it possible to overcome these difficulties and facilitate the creation of a secure global communication grid. The comparison of classical and quantum cryptography illustrates an evolution from complexity-based security to quantum physics security, which is immune to quantum computational attacks. Reports of quantum supremacy and QKD network development are stages on the way towards quantum computers and cryptography. Technology challenges on this approach include scalability of quantum systems, qubit coherence, and error correction. Quantum computing has the potential to transform computer and communication security. This route involves innovation in quantum technology development, as well as integration issues. Quantum computing and cryptography's full potential would lead to an age of new computational possibilities that will change the encryption of data and secure communication. © 2024 ieee.

关键词： Quantum cryptography

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Technology-Aware Drift Resilience Analysis of RRAM Crossbar Array Configurations 21

Technology-Aware Drift Resilience Analysis of RRAM Crossbar ...

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21st ieee Interregional NEWCAS conference (NEWCAS)

作者： Reiser, Daniel Reichenbach, Marc Rizzi, Tommaso Baroni, Andrea Fritscher, Markus Wenger, Christian Zambelli, Cristian Bertozzi, Davide BTU Cottbus Senftenberg D-03046 Cottbus Germany IHP Leibniz Inst Innovat Mikroelekt D-15236 Frankfurt Germany Univ Ferrara Dipartimento Ingn I-44122 Ferrara Italy

ISBN: (纸本)9798350300246

In-memory computing with resistive-switching random access memory (RRAM) crossbar arrays has the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. However, RRAM devices are subject to several non-idealities that result in significant inference accuracy drops compared with software baseline accuracy. A critical one is related to the drift of the conductance states appearing immediately at the end of program and verify algorithms that are mandatory for accurate multi-level conductance operation. The support of drift models in state-of-the-art simulation tools of memristive computationin-memory is currently only in the early stage, since they overlook key device- and array-level parameters affecting drift resilience such as the programming algorithm of RRAM cells, the choice of target conductance states and the weight-to-conductance mapping scheme. The goal of this paper is to fully expose these parameters to RRAM crossbar designers as a multi-dimensional optimization space of drift resilience. For this purpose, a simulation framework is developed, which comes with the suitable abstractions to propagate the effects of those RRAM crossbar configuration parameters to their ultimate implications over inference performance stability.

关键词： RRAM Neural Network Hardware In-Memory computing

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