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 representation provides a foundation for knowledge reasoning and applications by mapping entities and relations into vector space. Federated KG embedding enables the utilization of knowledge from diverse client sources while safeguarding the privacy of local data. However, due to demands such as privacy protection and the need to adapt to dynamic data changes, investigations into machine unlearning (MU) have been sparked. However, it is challenging to maintain the performance of KG embedding models while forgetting the influence of specific forgotten data on the model. In this paper, we propose FedDM, a novel framework tailored for machine unlearning in federated knowledge graphs. Leveraging diffusion models, we generate noisy data to sensibly mitigate the influence of specific knowledge on FL models while preserving the overall performance concerning the remaining data. We conduct experimental evaluations on benchmark datasets to assess the efficacy of the proposed model. Extensive experiments demonstrate that FedDM yields promising results in knowledge forgetting.

翻译：联邦学习（FL）通过支持模型共享与协作，在保障数据隐私的同时推动了人工智能技术的发展与应用。知识图谱（KG）通过将实体和关系映射到向量空间，为知识推理与应用提供了基础嵌入表示。联邦知识图谱嵌入技术能够在保护本地数据隐私的前提下，整合来自不同客户端的知识资源。然而，隐私保护需求及对动态数据变化的适应性要求，催生了机器遗忘（MU）研究。当前挑战在于：在遗忘特定数据对模型影响的同时，如何维持知识图谱嵌入模型的性能。本文提出FedDM——一种专为联邦知识图谱中机器遗忘任务设计的新型框架。该框架利用扩散模型生成噪声数据，在合理削弱特定知识对联邦学习模型影响的同时，保持剩余数据对应的整体模型性能。我们在基准数据集上开展实验评估以验证模型效能，大量实验表明FedDM在知识遗忘任务中取得了显著成效。