Towards federated learning over large-scale streaming data
| dc.contributor.author | Pereira, Aaron, author | |
| dc.contributor.author | Pallickara, Sangmi, advisor | |
| dc.contributor.author | Pallickara, Shrideep, committee member | |
| dc.contributor.author | Zahran, Sammy, committee member | |
| dc.date.accessioned | 2020-06-22T11:52:34Z | |
| dc.date.available | 2020-06-22T11:52:34Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | 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. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Pereira_colostate_0053N_15904.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/208427 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | 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. | |
| dc.title | Towards federated learning over large-scale streaming data | |
| dc.type | Text | |
| dcterms.rights.dpla | 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). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Pereira_colostate_0053N_15904.pdf
- Size:
- 1.7 MB
- Format:
- Adobe Portable Document Format