In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

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 …