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检索条件"主题词=distributed databases"

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Clustered Federated Learning with Non-IID Data: Mitigating Accuracy Overestimates Through Hold-Out Model Selection and Evaluation

Clustered Federated Learning with Non-IID Data: Mitigating A...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Davoud Gholamiangonabadi Katarina Grolinger Electrical and Computer Engineering Western University London Ontario Canada

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Federated learning (FL), a distributed learning strategy, improves security and privacy by eliminating the need for clients to share their local data; however, FL struggles with non-IID (non independent and identically distributed) data. Clustered FL aims to remedy this by grouping similar clients and training a model per group; nevertheless, it faces difficulties in determining clusters without sharing local data and conducting model evaluation. Clustered FL evaluation on unseen clients typically applies all models, selecting the best-performer for each client - approach known as best-fit cluster evaluation. This paper challenges such evaluation process arguing that it violates a fundamental machine learning principle: test dataset labels should be used only for performance calculation, not for model selection. We show that best-fit cluster evaluation results in significant accuracy overestimates. Moreover, we present an evaluation approach that maintains the separation between model selection and evaluation by reserving a portion of the target client data for model selection, while the remaining data is used for accuracy estimation. Experiments on four datasets, encompassing various IID and non-IID scenarios, demonstrate that the best-fit cluster evaluation produces overestimates that are statistically different from our evaluation.

关键词： Training Data privacy Accuracy Federated learning Distance learning Estimation distributed databases Data models Security Faces

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Workload characteristics for process migration and load balancing

Workload characteristics for process migration and load bala...

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International Conference on distributed Computing Systems

作者： M. Nuttall M. Sloman Department of Computing Imperial College London London UK

Is process migration useful for load balancing? We present experimental results indicating that the answer to this question depends largely on the characteristics of the applied workload. Experiments with our Shiva system, which supports remote execution and process migration, show that only those CPU bound workloads which were generated using an unrealistic exponential distribution for execution times show improvements for dynamic load balancing. (We use the term 'dynamic' to indicate remote execution determined at and not prior to run time. The latter is known as 'static' load balancing.) Using a more realistic workload distribution and adding a number of short lived tasks prevents dynamic algorithms from working. Migration is only useful with heterogeneous workloads. We find the migration of executing tasks to remote data to be effective for balancing I/O bound workloads, and indicate the region of 'workload variable space' for which this migrate-to-data approach is useful.

关键词： Load management Heuristic algorithms Transaction databases distributed databases Educational institutions Exponential distribution Vents Yarn Web server Database systems

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Synchronization of Signals with Sampling Rate Offset and Missing Data Using Dynamic Programming Matching

Synchronization of Signals with Sampling Rate Offset and Mis...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Hayato Takeuchi Takao Kawamura Nobutaka Ono Shoko Araki Tokyo Metropolitan University Tokyo Japan NTT Corporation Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

In this paper, we propose a blind synchronization method for signals with sampling rate offset (SRO) and missing data, which occasionally occurs in distributed recording for acoustic scene classification. In our method, the correspondence between short-time frames is first estimated using cross-correlation and dynamic programming (DP) matching. Then, two methods for producing synchronized signals are compared. The first method is based on the overlap-add along the DP path, while the second method uses the DP path only to identify missing data positions and compensates for the SRO with a linear phase model. The performance of these methods is evaluated through experiments. The results are promising, and further applications to acoustic scene classification are expected.

关键词： Scene classification Asia distributed databases Information processing Parallel processing Acoustics Data models Dynamic programming Recording Synchronization

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Performance analysis of coding using distributed Turbo codes

Performance analysis of coding using distributed Turbo codes

引用

International Workshop on RFID And Adaptive Wireless Sensor Networks (RAWSN)

作者： Anas Aboudeine Fouad Ayoub Anouar Yatribi Mohammed Benattou Ibn Tofal University LRIT Lab Sciences Fatulty Kenitra Morocco CRMEF Kenitra Morocco Mohammed V University SIME Lab TSE Team ENSIAS Rabat Morocco

Nowadays the need for fast and reliable communication is increasing, which leads us to look for new ways to enhance channel coding. In this paper, we will study the case of a distributed coding between two users that aim to transmit data to a common destination, where each user transmits a partial redundancy to the destination, and relies on the second user for the remaining. The purpose of distributing the redundancy creation and transmission, is to benefit from each user channel quality for a more accurate decoding. In the context of our analysis, we will use a 1/2 rate convolutional code with between users and a distributed Turbo code for transmission to the destination. However, this study will aim to highlight the different key factors, as well as the advantage of choosing a distributed encoding.

关键词： Redundancy distributed databases Decoding Signal to noise ratio Convolutional codes Turbo codes

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COMET: A Recipe for Learning and Using Large Ensembles on Massive Data

COMET: A Recipe for Learning and Using Large Ensembles on Ma...

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IEEE International Conference on Data Mining (ICDM)

作者： Justin D. Basilico M. Arthur Munson Tamara G. Kolda Kevin R. Dixon W. Philip Kegelmeyer Sandia National Laboratories Livermore CA USA

COMET is a single-pass MapReduce algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed blocks of data and merges them into a mega-ensemble. This approach is appropriate when learning from massive-scale data that is too large to fit on a single machine. To get the best accuracy, IVoting should be used instead of bagging to generate the training subset for each decision tree in the random forest. Experiments with two large datasets (5GB and 50GB compressed) show that COMET compares favorably (in both accuracy and training time) to learning on a sub sample of data using a serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble evaluation which dynamically decides how many ensemble members to evaluate per data point, this can reduce evaluation cost by 100X or more.

关键词： Training Accuracy Vegetation Computational modeling Bagging Decision trees distributed databases

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Application of Grid Technology in Resource Storage of Digital Library

Application of Grid Technology in Resource Storage of Digita...

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International Conference on Management and Service Science, MASS

作者： Li-hua Zhu Library Hebei University of Engineering Handan China

The paper analyzed the characteristics of digital library resources and the requirements for *** gave the technology and characteristics of grid storage ,analyzed the advantages of the grid storage, and discussed the ... 详细信息

关键词： Libraries distributed databases Memory Dynamic scheduling Storage area networks Scalability

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Set avoidance probabilities and bounds on the reliability of erasure coded storage systems

Set avoidance probabilities and bounds on the reliability of...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Antonio Campello Vinay A. Vaishampayan Institute of Mathematics University of Campinas São Paulo Brazil Rutgers The State University of New Jersey New Brunswick NJ US

Bounds are developed on the probability that the Cartesian product of a given number of finite random sets does not intersect (avoids) a given fixed set. These bounds are then used to estimate the probability of data loss in a distributed storage system that uses erasure codes to protect against data loss when disks fail. These are the first bounds on the probability of data loss that we are aware of. We compare our upper bound on the probability of data loss to approximations that are used in the literature, and show that our bounds are tighter and the gap is significant in some cases. Our bounds also suggest that in some cases, a more efficient (higher rate) code will suffice to meet a data loss probability target than that predicted by approximations widely used in the industry.

关键词： Upper bound distributed databases Maintenance engineering Random variables Reliability theory

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Large-Scale Participatory Urban Sensing: A Fad or Reality?

Large-Scale Participatory Urban Sensing: A Fad or Reality?

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International Conference on Mobile Data Management

作者： Archan Misra Juha Laurila School of Information Systems Singapore Management University Singapore Nokia Research Center Finland

This paper describes the topic and participants of the MDM 2013 panel.

关键词： Mobile communication Educational institutions Sensors distributed databases Conferences Mobile computing Computational modeling

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Intelligent Data Placement Mechanism for Replicas Distribution in Cloud Storage Systems

Intelligent Data Placement Mechanism for Replicas Distributi...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Ibrahim Adel Ibrahim Wei Dai Mustafa Bassiouni University of Central Florida Orlando FL US

ISBN: (纸本)9781509052646

The Hadoop distributed File System (HDFS) is a distributed storage system that stores large volumes of data reliably and provide access to the data by the applications at high bandwidth. HDFS provides high reliability and availability by replicating data, typically three copies, and distribute these replicas across multiple data nodes. The placement of data replicas is one of the key issues that affect the performance of HDFS. In the current HDFS replica placement policy the replicas of data blocks cannot be evenly distribute across cluster nodes, so the current HDFS has to rely on load balancing utility to balance replica distributions which results in more time and resources consuming. These challenges drive the need for intelligent methods that solve the data placement problem to achieve high performance without the need for load balancing utility. In this paper, we propose an intelligent policy for data placement in cloud storage systems addressing the above challenges.

关键词： distributed databases Cloud computing File systems Clustering algorithms Load management Reliability Bandwidth

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Congestion field detection for Service Quality improvement using Kernel density estimation

Congestion field detection for Service Quality improvement u...

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Yuki Shitara Tatsuya Yamazaki Kyoko Yamori Takumi Miyoshi Graduate School of Science & Technology Niigata University Niigata Japan College of Systems Engineering and Science Shibaura Institute of Technology Saitama Japan Department of Business Administration Asahi University Gifu Japan

ISBN: (纸本)9781467390576

Owing to proliferation of smart phones, communication services such as a video streaming service are common in a mobile situation. For these services, quality evaluation and communication control based on Quality of Experience (QoE), which is the degree of a user's subjective satisfaction, is very important because the final goal of delivering high-quality service is improving user's satisfaction. QoE tends to be affected by several factors including Quality of Service (QoS). Therefore, collecting QoS data from the users who use the mobile application has become one of promising schemes to meet the QoE, which is called the crowd sourcing data. The crowd sourcing data are, however, apt to be affected by sensing errors and low accuracy. In this study, we propose to estimate some densities just from the records of application use and its location information to remove the sensing errors and low accuracy. The Kernel density estimator is used to derive a continuous density function from discrete distributed sample data such as the collected QoS data. From the viewpoint of QoS, it is important to extract high-density fields of the application use to find out QoS degradation. After estimating the Kernel density estimator, we determine the borderline between the high-density field and the other field by using the reference value that can be determined from the observed data. Simulated experiments verify effectiveness of the proposed method.

关键词： Kernel Quality of service Estimation Gaussian distribution Mobile communication Sensors distributed databases

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