Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, federated KG embedding can fully take advantage of knowledge learned from different clients while preserving the privacy of local data. However, realistic problems such as data heterogeneity and knowledge forgetting still remain to be concerned. In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between local optimization and global convergence caused by data heterogeneity, we propose mutual knowledge distillation to transfer local knowledge to global, and absorb global knowledge back. Moreover, we present an unlearning method based on cognitive neuroscience, which combines retroactive interference and passive decay to erase specific knowledge from local clients and propagate to the global model by reusing knowledge distillation. We construct new datasets for assessing realistic performance of the state-of-the-arts. Extensive experiments show that FedLU achieves superior results in both link prediction and knowledge forgetting.

翻译：联邦学习（FL）最近作为一种范 paradigm 兴起，用于在分布式客户端之间训练全局机器学习模型，而无需共享原始数据。知识图谱（KG）嵌入将知识图谱表示为连续向量空间，是许多知识驱动应用的支柱。作为有前景的结合，联邦知识图谱嵌入能够充分利用从不同客户端学习到的知识，同时保护本地数据的隐私。然而，现实问题如数据异构性和知识遗忘仍然需要关注。本文提出FedLU，一种用于异构知识图谱嵌入学习与反学习的新型联邦学习框架。为应对数据异构性导致的局部优化与全局收敛之间的偏移，我们提出互知识蒸馏，将局部知识迁移至全局，并吸收全局知识返回。此外，我们提出一种基于认知神经科学的反学习方法，结合逆向干扰与被动衰减，从本地客户端擦除特定知识，并通过重复利用知识蒸馏将其传播至全局模型。我们构建了新数据集，以评估当前最优方法的现实性能。大量实验表明，FedLU在链接预测与知识遗忘方面均取得了优越结果。