Federated Graph Unlearning
The demand for data privacy has led to the development of frameworks like Federated Graph Learning (FGL), which facilitate decentralized model training. However, a significant operational challenge in such systems is adhering to the right to be forgotten. This principle necessitates robust mechanisms for two distinct types of data removal: the selective erasure of specific entities and their associated knowledge from local subgraphs and the wholesale removal of a user's entire dataset and influence. Existing methods often struggle to fully address both unlearning requirements, frequently resulting in incomplete data removal or the persistence of residual knowledge within the system. This work introduces a unified framework, conceived to provide a comprehensive solution to these challenges. The proposed framework employs a bifurcated strategy tailored to the specific unlearning request. For fine-grained Meta Unlearning, it uses prototype gradients to direct the initial local forgetting process, which is then refined by generating adversarial graphs to eliminate any remaining data traces among affected clients. In the case of complete client unlearning, the framework utilizes adversarial graph generation exclusively to purge the departed client's contributions from the remaining network. Extensive experiments on multiple benchmark datasets validate the proposed approach. The framework achieves substantial improvements in model prediction accuracy across both client and meta-unlearning scenarios when compared to existing methods. Furthermore, additional studies confirm its utility as a plug-in module, where it materially enhances the predictive capabilities and unlearning effectiveness of other established methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph GenerationGraph LearningSimilar Papers 제목 키워드 기반
EFU: Enforcing Federated Unlearning via Functional Encryption
Federated unlearning (FU) algorithms allow clients in federated settings to exercise their ''right to be forgotten'' by removing the influence of their data from a collaboratively trained model. Existing FU methods maint…
Exploring Federated Unlearning: Review, Comparison, and Insights
The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems. However, developing federa…
Federated LearningPrivacy PreservingSurveyRethinking Federated Unlearning via the Lens of Memorization
Federated learning (FL) increasingly needs machine unlearning to comply with privacy regulations. However, existing federated unlearning approaches may overlook the overlapping information between the unlearning and rema…
Federated LearningFederated Knowledge Graph Unlearning via Diffusion Model
Federated learning (FL) promotes the development and application of artificial intelligence technologies by enabling model sharing and collaboration while safeguarding data privacy. Knowledge graph (KG) embedding represe…
Federated LearningKnowledge GraphsMachine UnlearningmodelForgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
With the increasing importance of data privacy and security, federated unlearning has emerged as a novel research field dedicated to ensuring that federated learning models no longer retain or leak relevant information o…
Knowledge DistillationFederated Learning