Graph Federated Learning: Necessity, Classification, Algorithms, Platform Architecture, and Financial Applications
This article provides a comprehensive overview of graph federated learning, covering its motivation, taxonomy, representative algorithms, platform design, practical financial use cases, and future research challenges, with a focus on privacy-preserving distributed graph neural network training.