Privacy-preserving design of graph neural networks with applications to vertical federated learning

Understand

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).

• The surging developments of graph representation learning (GRL) have opened up new opportunities for FRM applications under FL via efficiently utilizing the graph-structured data generated from underlying transaction networks.
• Meanwhile, transaction information is often considered highly sensitive.
• To prevent data leakage during training, it is critical to develop FL protocols with formal privacy guarantees.