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Titlebook: Cloud Computing – CLOUD 2019; 12th International C Dilma Da Silva,Qingyang Wang,Liang-Jie Zhang Conference proceedings 2019 Springer Nature

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Towards Decentralized Deep Learning with Differential Privacy,mance and privacy in the private setting. We evaluate LEASGD in real distributed testbed with poplar deep neural network models MNIST-CNN, MNIST-RNN, and CIFAR-10. Extensive experimental results show that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD by achieving nearly
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Class Indistinguishability for Outsourcing Equality Conjunction Search, that of bucketization but allows the cloud server to perform search of relevant data and filtering of false positives. We present a construction achieving these goals through a two-phase search algorithm for a query. The first phase finds a candidate set through a sub-linear search. The second phas
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https://doi.org/10.1007/978-3-476-04559-1s not depend on the number of working wavelengths. The new approach can significantly reduce the processing time of computing P-cycles in large scale optical, server-centric data center networks, e.g., BCube, FiConn, and DCell networks.
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The Paradox: Can God Commit Suicide?, be executing real proof-of-work workload along with peer-to-peer network communications and hash-based immutability. In addition, the proposed system employs a preprocessing approach to avoid the per-node computation overhead at runtime and thus achieves practical scales. We have evaluated the syst
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Lothar Gerritzen,Marius van der Putther. Furthermore, by processing the online social logs instantly, it allows online streaming data to be processed in a distributed manner, which reduces the online detection latency and avoids the inefficiency of outdated spam posts. Our experimental results with real-world social logs demonstrate
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Wolfgang Thomala,Karl-Heinz Kloosssed data, an improved parallel method is proposed for calculating the conditional probability that comprehensively considers the effects of the feature items in each document, class and training set. Finally, through experiments on different widely used corpuses on the Spark computation platform, t
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