这篇论文提出了FedLAB，用可追溯码本处理联邦多模态图学习的隐私问题，10个基准上最多提升了7.53%，很扎实的研究。
FedLAB提出了一种可追溯的语义码本框架，用于联邦多模态图基础学习。该框架将多模态图知识组织为类型化的分层码本，涵盖模态证据、节点语义和拓扑上下文。通过联邦语义质心预训练优化可追溯单元，同时保持原始多模态内容和图结构本地化。在10个基准和6个下游任务上，FedLAB相对现有方法提升最高7.53%。
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-centric and modality-centric tasks. In practice, however, such multimodal graphs are often distributed across decentralized clients, where raw contents and local structures cannot be centrally shared due to privacy constraints. This motivates federated multimodal graph foundation learning, which requires not only transferable representation learning but also intrinsic semantic traceability under strict data isolation. Existing methods usually exchange or store knowledge through parameters, prototypes, embeddings, or compact codebooks, which support optimization and transfer but do not explicitly expose how modality evidence, node semantics, and topology context jointly support predictions. To bridge this gap, we propose FedLAB, a traceable semantic codebook framework that organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context. FedLAB further refines these trace units through federated semantic barycenter pre-training while keeping raw multimodal contents and graph structures local. Extensive experiments on 10 benchmarks and 6 downstream tasks show that FedLAB improves over state-of-the-art baselines by up to 7.53\%, while preserving a native semantic trace interface.