Class-Wise Representation Based Federated Learning for Non-IID Graph Data

Federated Graph Learning (FGL) allows multiple participants to collaboratively train powerful Graph Neural Network (GNN) models in a distributed fashion without sharing raw data. It has shown advantages in various domains, including social network analysis and drug discovery. However, one of the core challenges in FGL is the nonIndependent and Identically Distributed (non-IID) nature of data. Particularly in graph-structured data, local clients may display heterogeneous feature distributions and skewed node degree distributions. To address this issue, we propose FedPT, a method within the FGL framework that incorporates the concept of prototype learning to aggregate feature representations across heterogeneous graph data through class prototypes. To capture core features of each class, each client integrates normalized and dimension-reduced features within each class, while the server performs global aggregation based on data weighting to harmonize class-specific core representations across clients. Subsequently, nodes are categorized based on their degrees, and hierarchical class vectors are computed and aggregated globally, allowing for finer-grained representation of graph structural differences. Finally, we design a contrastive learning approach to maximize the similarity between local and global prototypes of the same class and minimize the similarity between prototypes of different classes. We conduct extensive experiments on ten real-world datasets, demonstrating the superior performance of the FedPT method.