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 machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …

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 …

Federated Learning (FL), which has been employed to train machine learning models on the
data with a distributed manner, could enhance the immersive user experience for the human …

The recent advances in diffusion models (DMs) have revolutionized the generation of
complex and diverse images. However, these models also introduce potential safety …

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) …

We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …

Federated learning (FL) is a trending distributed learning framework that enables
collaborative model training without data sharing. Machine learning models trained on …

Federated Learning (FL) enables collaborative training of a Machine Learning (ML) model
across multiple parties, facilitating the preservation of users' and institutions' privacy by …