  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.
