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.

本文提出了 FedDM, 一种面向联邦知识图的模型遗忘的新框架，通过扩散模型生成噪声数据以合理地减轻特定知识对 FL 模型的影响，同时保持其对剩余数据的整体性能，并通过对基准数据集进行实验评估证明 FedDM 在知识遗忘方面具有良好的效果。

分布式知识图谱通过扩散模型实现遗忘

Federated Knowledge Graph Unlearning via Diffusion Model

Federated learning~(FL) has recently attracted increasing attention from
academia and industry, with the ultimate goal of achieving collaborative
training under privacy and communication constraints. Existing iterative model
averaging based FL algorithms require a large number of communication rounds to
obtain a well-performed model due to extremely unbalanced and non-i.i.d data
partitioning among different clients. Thus, we propose FedDM to build the
global training objective from multiple local surrogate functions, which
enables the server to gain a more global view of the loss landscape. In detail,
we construct synthetic sets of data on each client to locally match the loss
landscape from original data through distribution matching. FedDM reduces
communication rounds and improves model quality by transmitting more
informative and smaller synthesized data compared with unwieldy model weights.
We conduct extensive experiments on three image classification datasets, and
results show that our method can outperform other FL counterparts in terms of
efficiency and model performance. Moreover, we demonstrate that FedDM can be
adapted to preserve differential privacy with Gaussian mechanism and train a
better model under the same privacy budget.

本研究提出了 FedDM，旨在通过多个本地替代函数来构建全局训练目标，从而减少通信轮数，改善模型质量，并在保留差分隐私的同时证明该算法的有效性。