Federated learning for graph neural networks - Yan, Yige (Nanyang Technological University, 2023)
This dissertation investigates the combination of graph neural networks (GNNs) and federated learning (FL) for addressing practical problems while preserving data privacy and reducing computational complexity. Specifically, we reproduce the Graph Clustered Federated Learning (GCFL) framework and proposed the Privacy Preserving Graph Clustered Federated Learning (PP-GCFL) algorithm, which enhance model performance and privacy protection.