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.
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Dual-Phase Federated Deep Unlearning via Weight-Aware Rollback and Reconstruction
School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；School of Software, Nanjing University of Information Science and Technology（信息科学技术大学软件学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
University of Tokyo（东京大学）；Toyota Central Research Laboratory（丰田中央研究所）；University of California, Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；National Institute of Advanced Industrial Science and Technology（国家工业科学与技术研究院）
Towards Irreversible Machine Unlearning for Diffusion Models
Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系，设计与工程学院）；Betterdata, Singapore（新加坡Betterdata公司）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）
Roots Beneath the Cut: Uncovering the Risk of Concept Revival in Pruning-Based Unlearning for Diffusion Models
University of Georgia（佐治亚大学）；Carnegie Mellon University（卡内基梅隆大学）；Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Arizona（亚利桑那大学）
Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning
Nanyang Technological University, Singapore（南洋理工大学，新加坡）；Central South University, China（中南大学，中国）；Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China（航空航天信息安全部门，教育部，武汉大学，中国）；State Key Laboratory of Blockchain and Data Security, Zhejiang University, China（区块链与数据安全国家重点实验室，浙江大学，中国）
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Southeast University（东南大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, Berkeley（加州大学伯克利分校）；Nanyang Technological University（南洋理工大学）
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
University of Macau（澳门大学）；University Of Electronic Science And Technology（电子科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
School of Cyber Engineering, Xidian University（电子科技大学信息工程学院）；RMIT University（皇家墨尔本理工大学）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（澳大利亚联邦科学与工业研究组织）；UNSW Sydney（新南威尔士大学悉尼分校）；University of California, Davis（加州大学戴维斯分校）
