Rethinking Client-oriented Federated Graph Learning

As a new distributed graph learning paradigm, Federated Graph Learning (FGL) facilitates collaborative model training across local systems while preserving data privacy.We review existing FGL approaches and categorize their optimization mechanisms into:(1) Server-Client (S-C), where clients upload local model parameters for server-side aggregation and global updates;(2) Client-Client (C-C), which allows direct exchange of information between clients and customizing their local training process.We reveal that C-C shows superior potential due to its refined communication structure.However, existing C-C methods broadcast redundant node representations, incurring high communication costs and privacy risks at the node level. To this end, we propose FedC4, which combines graph Condensation with C-C Collaboration optimization. Specifically, FedC4 employs graph condensation technique to refine the knowledge of each client's graph into a few synthetic embeddings instead of transmitting node-level knowledge. Moreover, FedC4 introduces three novel modules that allow the source client to send distinct node representations tailored to the target client's graph properties. Experiments on eight public real-world datasets show that FedC4 outperforms state-of-the-art baselines in both task performance and communication cost. Our code is now available onthis https URL.