FedGNN-Federated GNN Tool

Example

Collecting and normalizing electric power load data from smart grids across different regions.

Scenario

Utility companies collect diverse data, including load demands, weather conditions, and industrial outputs. This data is normalized to ensure consistency and prepare for further processing, enabling a comprehensive understanding of power usage patterns.

Example

Building a graph structure that represents the electric power system and aggregating neighbor features using GraphSAGE.

Scenario

Each utility company constructs a local graph where nodes represent transmission towers, and edges denote the power lines between them. Using GraphSAGE, a GNN architecture, features from neighboring nodes are aggregated to capture the local structural information within the electrical grid, facilitating the identification of critical load influencers.

Example

Forming super nodes through feature fusion and training a federated model across multiple clients.

Scenario

Utility companies identify key subgraphs within their local graphs, which are then fused into super nodes that encapsulate essential structural and feature information. These super nodes are used in a federated manner to train a global model that predicts electric power loads with high accuracy, leveraging cross-regional insights without exchanging raw data.

Example

Creating a global graph structure by linking super nodes across clients and optimizing the model through edge pruning based on Pearson correlation coefficients.

Scenario

The federated model constructs a global graph by connecting super nodes from all participating clients, simulating inter-regional interactions. The model is refined by selectively pruning edges, focusing on the most significant connections to enhance prediction accuracy. This process allows utility companies to leverage collective intelligence for optimal load forecasting.