AI 中文总结
针对联邦开放世界多模态图学习的灾难性遗忘问题，本文提出FedOGL框架，通过客户端记忆保留与服务器原型共享，使性能下降降低42.67%，同时维持或提升下游任务性能。
AI 中文摘要
联邦图学习可在不共享原始图信息的前提下，对分散式图数据开展协同训练。随着风险演变，客户端需从私有多模态图流中学习新类别、保留历史类别，并拒绝已知类别空间外的样本。在此场景下，客户端需从私有多模态图流中学习新类别，同时保留历史类别并拒绝当前已知类别空间外的样本。核心挑战为灾难性遗忘，在联邦多模态图中，该问题不仅是分类器层面的失效：旧知识可能因模态-语义覆盖、拓扑诱导的结构侵蚀及联邦记忆碎片化而被擦除。为应对此挑战，本文提出语义-结构记忆保留框架FedOGL。客户端侧，FedOGL通过重放和任务启动蒸馏保留历史决策行为，同时通过投影到全局共享结构基来保护图传播记忆；服务器侧，FedOGL维护并传输紧凑的类别原型，以促进跨客户端知识共享且不暴露原始图数据。大量实验表明，与表现最佳的基线相比，FedOGL使灾难性遗忘导致的性能下降降低了42.67%，同时在下游任务上保持或提升了性能。
英文摘要
Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clients must learn emerging classes from private multimodal graph streams, retain historical categories, and reject samples outside the known class space. In this setting, clients must learn emerging classes from private multimodal graph streams while preserving historical categories and rejecting samples outside the current known class space. The core challenge is catastrophic forgetting, which in federated multimodal graphs is not merely a classifier-level failure: old knowledge can be erased through modality-semantic overwriting, topology-induced structural erosion, and federated memory fragmentation. To address this challenge, we propose \textbf{FedOGL}, a semantic-structural memory preservation framework. On the client side, FedOGL preserves historical decision behavior through replay and task-start distillation, while protecting graph-propagation memory via projection onto a globally shared structure basis. On the server side, FedOGL maintains and transfers compact category prototypes to facilitate cross-client knowledge sharing without exposing raw graph data. Extensive experiments demonstrate that, compared with the best-performing baselines, FedOGL reduces performance degradation caused by catastrophic forgetting by \textbf{42.67\%}, while maintaining or improving performance on downstream tasks.
Comments8 pages, 7 figures
