AI 中文总结
针对现有联邦图遗忘无法满足多模态细粒度遗忘请求的问题，提出MMFGU框架，通过目标特定表示解耦等技术解决三大挑战，实验显示其能有效移除请求信息并实现41.5倍加速。
AI 中文摘要
多模态联邦图学习允许客户端在不共享私有本地数据的情况下，基于结构、文本和视觉信号协同训练图模型。然而，异构多模态内容的存在使得遗忘请求更频繁且更细粒度：用户可能删除账户或交互、移除特定图像或文本同时保留关联实体，或撤销保留模态与图属性间已学习的对应关系。现有联邦图遗忘主要处理实体/关系或客户端移除，无法直接满足这些多模态请求，带来三大挑战：仅移除请求信息而不损坏保留内容、防止目标通过剩余模态或图邻域被恢复、阻止其他客户端的相关痕迹在聚合后重新进入全局模型。为解决这些问题，本文提出MMFGU，一种围绕目标特定表示解耦构建的多模态联邦图遗忘框架。MMFGU将异构请求映射为统一目标载体，在锚定保留语义的同时解耦请求表示，通过轻量级探针暴露并修复传播的残差，还通过紧凑原型和响应信号选择性清除受影响的客户端。实验表明，MMFGU能有效移除请求信息、保留保留图的效用，且相比完全重训练实现了41.5倍的加速。
英文摘要
Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data. However, the presence of heterogeneous multimodal content also makes unlearning requests more frequent and fine-grained: users may delete accounts or interactions, remove a particular image or text while retaining the associated entity, or revoke the learned correspondence between retained modalities or graph attributes. Existing federated graph unlearning mainly handles entity/relation or client removal and cannot directly satisfy these multimodal requests. They introduce three challenges: removing only the requested information without damaging retained content, preventing the target from being recovered through remaining modalities or graph neighborhoods, and stopping related traces on other clients from re-entering the global model after aggregation. To address them, we propose \textsc{\textbf{MMFGU}}, a multimodal federated graph unlearning framework built around target-specific representation decoupling. \textsc{MMFGU} maps heterogeneous requests into unified target carriers, decouples requested representations while anchoring retained semantics, exposes and repairs propagated residuals with lightweight probes, and selectively purges affected clients through compact prototype and response signals. Experiments show that \textsc{MMFGU} effectively removes requested information, preserves retained graph utility, and achieves a $\boldsymbol{41.5\times}$ speedup over full retraining.
