MMFGU: Multimodal Federated Graph Unlearning
MMFGU is a novel framework for multimodal federated graph unlearning that addresses fine-grained unlearning requests beyond simple entity or client removal The framework uses target-specific representation decoupling to map heterogeneous unlearning requests into unified target carriers while preserving retained semantics MMFGU introduces lightweight probes to expose and repair propagated residuals, and selectively purges affected clients through compact prototype and response signals Experimenta
Analysis
TL;DR
- MMFGU is a novel framework for multimodal federated graph unlearning that addresses fine-grained unlearning requests beyond simple entity or client removal
- The framework uses target-specific representation decoupling to map heterogeneous unlearning requests into unified target carriers while preserving retained semantics
- MMFGU introduces lightweight probes to expose and repair propagated residuals, and selectively purges affected clients through compact prototype and response signals
- Experimental results demonstrate effective removal of requested information, preservation of retained graph utility, and a 41.5x speedup over full retraining
Why It Matters
This work addresses a critical gap in federated learning systems where multimodal data (structural, textual, visual) is increasingly common but unlearning capabilities remain limited to coarse-grained operations. As privacy regulations like GDPR enforce "right to be forgotten" requirements, the ability to perform fine-grained unlearning in multimodal federated graph settings becomes essential for compliant and ethical AI deployment.
Technical Details
- MMFGU handles three types of fine-grained unlearning requests: account/interaction deletion, modality-specific removal (e.g., removing an image while retaining the entity), and correspondence revocation between retained modalities or graph attributes
- The core technique involves target-specific representation decoupling, which maps heterogeneous requests into unified target carriers and decouples requested representations while anchoring retained semantics
- Lightweight probes are used to expose and repair propagated residuals that leak unlearned information through remaining modalities or graph neighborhoods
- Selective purging of affected clients is achieved through compact prototype and response signals, preventing related traces from re-entering the global model after aggregation
- The framework achieves a 41.5x speedup over full retraining while maintaining graph utility for retained data
Industry Insight
- Federated graph learning systems should incorporate fine-grained unlearning capabilities to comply with evolving privacy regulations and user expectations around data control
- The target-specific representation decoupling approach could be adapted to other multimodal federated learning scenarios beyond graph-structured data
- The 41.5x speedup over full retraining demonstrates that efficient unlearning is achievable without sacrificing model utility, making it practical for production deployment in privacy-sensitive applications
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