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https://hdl.handle.net/10356/151517
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jason Zhi Xin | en_US |
dc.contributor.author | Ng, Kai Chin | en_US |
dc.contributor.author | Toh, Arnold Xuan Ming | en_US |
dc.date.accessioned | 2021-07-06T06:12:21Z | - |
dc.date.available | 2021-07-06T06:12:21Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Lee, J. Z. X., Ng, K. C. & Toh, A. X. M. (2021). Personalised federated learning with differential privacy and gradient selection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151517 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/151517 | - |
dc.description.abstract | The fast-emerging field of federated learning holds the promise of allowing clients to contribute to a central machine learning model without the need to send their data to a central server, thus providing privacy for their data. Two issues arise: dealing with statistical heterogeneity in datasets, which is often the case in real-world settings, and obtaining stricter data privacy through employing privacy-preserving mechanisms. In this paper, we use personalized layers in a federated Convolutional Neural Network model to address statistical heterogeneity and use differential privacy to provide mathematically rigorous privacy guarantees for the federated learning model. We also propose a gradient selection technique to increase model performance. We developed a framework combining these techniques and experimentally demonstrate the effectiveness of the proposed framework on a dataset in improving model performance whilst maintaining a reasonable level of privacy guarantee and training efficiency. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Nanyang Technological University | en_US |
dc.subject | Engineering::Computer science and engineering::Data | en_US |
dc.title | Personalised federated learning with differential privacy and gradient selection | en_US |
dc.type | Final Year Project (FYP) | en_US |
dc.contributor.supervisor | Lam Kwok Yan | en_US |
dc.description.degree | Bachelor of Engineering Science (Chemical and Biomolecular Engineering) | en_US |
dc.description.degree | Bachelor of Engineering Science (Computer Science) | en_US |
dc.contributor.research | Renaissance Engineering Programme | en_US |
dc.contributor.supervisoremail | kwokyan.lam@ntu.edu.sg | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | Renaissance Capstone Project (RCP) |
Files in This Item:
File | Description | Size | Format | |
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Final Report.pdf Restricted Access | 1.1 MB | Adobe PDF | View/Open |
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