Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/153240
Title: | Federated graph neural network | Authors: | Koh, Tat You @ Arthur | Keywords: | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence | Issue Date: | 2021 | Publisher: | Nanyang Technological University | Source: | Koh, T. Y. @. A. (2021). Federated graph neural network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153240 | Project: | SCSE20-0749 | Abstract: | Graph Neural Networks is a form of machine learning that has seen significant growth in popularity and use, owing to their natural affinity for capturing implicit representations that exist in real-world phenomena. Many of these real-world phenomena involve people-centric data, which are privacy-sensitive. Because of this, there are growing privacy concerns pertaining to the use of machine learning for privacy-sensitive data, resulting in regulations that discourage or even prevent centralized collection of people-centric data. In this project, we implement and introduce a possible alternative means of conducting Graph Neural Network machine learning on privacy-sensitive data by combining a form of de-centralized, privacy-preserving machine learning known as Federated Learning with Graph Neural Networks. Our approach is showcased through the augmentation of the GCN and GraphSAGE GNNs with FL. These augmented FL-GNN models are able perform privacy-preserving de-centralized learning through a server-client architecture that does not require the collection of user data to train a Graph Neural Network model. | URI: | https://hdl.handle.net/10356/153240 | Schools: | School of Computer Science and Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FYP_KOH TAT YOU @ ARTHUR KOH_Federated Graph Neural Network.pdf Restricted Access | Federated Graph Neural Network FYP Report | 702.51 kB | Adobe PDF | View/Open |
Page view(s)
209
Updated on Mar 29, 2024
Download(s) 50
27
Updated on Mar 29, 2024
Google ScholarTM
Check
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.