… federated unlearning method to eliminate a client’s contribution by subtracting the accumulated
historical updates from the model and leveraging the knowledge distillation … unlearning …

… unlearning … knowledge distillation process. Both these methods require the server to
keep the history of the parameter updates from all the clients. Moreover, the knowledge-distillation …

… construction of federated unlearning. In [132], the concept of knowledge editing throughout
… process in the averaged model, knowledge distillation is employed. This technique facilitates …

… unlearning process into two steps: knowledge erasure and memory guidance. We first propose
a novel knowledge … the model performance through knowledge distillation. An advantage …

… mutual knowledge distillation to transfer local knowledge to … federated KG embedding
learning and unlearning framework. In federated learning, we design mutual knowledge distillation …

… background knowledge relevant to Federated Unlearning (… to Federated Learning (FL)
and Machine Unlearning (MU), … Takeaways on knowledge distillation: Knowledge distillation …

… , Zhu et al [8] propose a mutual knowledge distillation method to transfer local knowledge
to the global and absorb knowledge to the global. Because of the requirements of privacy …

… ofthe-art federated machine unlearning models. Knowledge Distillation is a federated unlearning
… from the model and leveraging the knowledge distillation method to restore the model’s …

… unlearning in federated recommendation systems, we propose an efficient unlearning method
FRU (Federated … To the best of our knowledge, this is the first work to investigate machine …

… To the best of our knowledge, this is the first formal definition of exact federated … recent
works have proposed approximate unlearning techniques based on knowledge distillation [30], …