FEDERATED LEARNING ON DISTRIBUTED GRAPHS CONSIDERING MULTIPLE HETEROGENEITIES

Federated graph learning (FGL) collaboratively learns a global graph neural network with distributed graphs, where a significant challenge is addressing non-IID issues. Existing work has not fully explored and utilized the intrinsic features of graphs, resulting in their inability to effectively solve non-IID issues. To tackle this challenge, we investigate for the first time the various heterogeneity that causes non-IID issues in FGL and how they can be utilized to alleviate the issues, including the heterogeneity of nodes and structures as basic components of the graph, as well as the resulting heterogeneity in the representations of the graph. Furthermore, we propose ProtoFGL to address these issues. ProtoFGL first extracts heterogeneous features of nodes and structures from local data and incorporates them into prototypes, which are then used as graph representations for collaborative training. Experimental results show that ProtoFGL outperforms state-of-the-art methods in node classification tasks in accuracy and F1 score.