Federated learning (FL), introduced in 2017, facilitates collaborative learning between non- trusting parties with no need for the parties to explicitly share their data among themselves …
The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) …
With the development of trustworthy Federated Learning (FL), the requirement of implementing right to be forgotten gives rise to the area of Federated Unlearning (FU) …
Federated Learning (FL) enables collaborative training of a Machine Learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by …
The rapid advancement of diffusion models (DMs) has not only transformed various real- world industries but has also introduced negative societal concerns, including the …
Abstract Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure …
Abstract Machine unlearning aims to remove specific knowledge from a well-trained machine learning model. This topic has gained significant attention recently due to the …