Federated multimodal graph learning can achieve strong performance while maintaining privacy and interpretability by organizing knowledge into typed semantic codebooks that explicitly track how different modalities and graph structure contribute to predictions.
FedLAB enables federated learning on multimodal graphs (graphs with text, images, and attributes) while preserving privacy by organizing knowledge into traceable semantic codebooks.