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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.
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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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