Federated deep learning for edge computing (part I)
With the increase in various usages of AI, comes new forms of training and deployment. One such advancement is coined as ‘federated learning’. Federated learning is an environment which consists of a central node that is connected through a network setting to multiple edge nodes to enable asynchr...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138113 |
| _version_ | 1811686335966085120 |
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| author | See, Ian Soong En |
| author2 | Tan Rui |
| author_facet | Tan Rui See, Ian Soong En |
| author_sort | See, Ian Soong En |
| collection | NTU |
| description | With the increase in various usages of AI, comes new forms of training and deployment. One such
advancement is coined as ‘federated learning’. Federated learning is an environment which
consists of a central node that is connected through a network setting to multiple edge nodes to
enable asynchronous model training. A main advantage of the federated learning framework is that
data privacy of edge nodes is preserved throughout training.
However, there are certain problems that are specific to Federated learning. One of these problems
is network cost; of which this combined project hopes to solve by creating an efficent scheduling
algorithm. This algorithm is being presented in part II of this project. The main purpose of this
report is to detail the step by step process in the creation of a virtual environment that be used as a
platform that supports testing and developing algorithms within federated learning. The virtual
environment was created using the Mininet virtual image in conjunction with the Pysyft library.
The output of part I of this project creates a virtual image (OVA format) that can be launched on
a hypervisor such as oracle virtual box. |
| first_indexed | 2024-10-01T04:58:47Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138113 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:58:47Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1381132020-04-24T07:33:25Z Federated deep learning for edge computing (part I) See, Ian Soong En Tan Rui School of Computer Science and Engineering tanrui@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computer systems organization::Computer system implementation With the increase in various usages of AI, comes new forms of training and deployment. One such advancement is coined as ‘federated learning’. Federated learning is an environment which consists of a central node that is connected through a network setting to multiple edge nodes to enable asynchronous model training. A main advantage of the federated learning framework is that data privacy of edge nodes is preserved throughout training. However, there are certain problems that are specific to Federated learning. One of these problems is network cost; of which this combined project hopes to solve by creating an efficent scheduling algorithm. This algorithm is being presented in part II of this project. The main purpose of this report is to detail the step by step process in the creation of a virtual environment that be used as a platform that supports testing and developing algorithms within federated learning. The virtual environment was created using the Mininet virtual image in conjunction with the Pysyft library. The output of part I of this project creates a virtual image (OVA format) that can be launched on a hypervisor such as oracle virtual box. Bachelor of Engineering (Computer Science) 2020-04-24T07:33:25Z 2020-04-24T07:33:25Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138113 en SCSE19-0051 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computer systems organization::Computer system implementation See, Ian Soong En Federated deep learning for edge computing (part I) |
| title | Federated deep learning for edge computing (part I) |
| title_full | Federated deep learning for edge computing (part I) |
| title_fullStr | Federated deep learning for edge computing (part I) |
| title_full_unstemmed | Federated deep learning for edge computing (part I) |
| title_short | Federated deep learning for edge computing (part I) |
| title_sort | federated deep learning for edge computing part i |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computer systems organization::Computer system implementation |
| url | https://hdl.handle.net/10356/138113 |
| work_keys_str_mv | AT seeiansoongen federateddeeplearningforedgecomputingparti |