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oai:mountainscholar.org:10217/2084272025-12-30T03:28:50Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_182111col_10217_100389

Pereira, Aaron, author Pallickara, Sangmi, advisor Pallickara, Shrideep, committee member Zahran, Sammy, committee member 2020-06-22T11:52:34Z 2020-06-22T11:52:34Z 2020 Pereira_colostate_0053N_15904.pdf https://hdl.handle.net/10217/208427 https://doi.org/10.25675/3.02462 Distributed Stream Processing Engines (DSPEs) have seen significant deployment growth along with an increase in streaming data sources such as sensor networks. These DSPEs enable processing large amounts of streaming data in a cluster of commodity machines to extract knowledge and insights in real-time. Due to fluctuating data arrival rates in real-world applications, modern DSPEs often provide auto-scaling. However, the existing designs of advanced analytical frameworks are not effectively aligned with scalable streaming computing environments. We have designed and developed ORCA, a federated learning architecture that supports the training of traditional Artificial Neural Networks as well as Convolutional Neural Networks and Long Short-term Memory Network based models while ensuring resiliency during scaling. ORCA also introduces dynamic adjustment of the 'elasticity' hyper-parameter for rescaled computing environments. We estimate this elasticity hyper-parameter using reinforcement learning. Our empirical benchmarks show that ORCA is capable of achieving an MSE of 0.038 over real-world streaming datasets. born digital masters theses English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Towards federated learning over large-scale streaming data Text Master of Science (M.S.) Masters Computer Science Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access