As the right to be forgotten has been legislated worldwide, many studies attempt to design
machine unlearning mechanisms to enable data erasure from a trained model. Existing …

Federated Learning (FL) is designed to protect the data privacy of each client during the
training process by transmitting only models instead of the original data. However, the …

In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …

Federated unlearning has emerged very recently as an attempt to realize “the right to be
forgotten” in the context of federated learning. While the current literature is making efforts on …

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

The emergence of the right to be forgotten has sparked interest in federated unlearning.
Researchers utilize federated unlearning to address the issue of removing user …

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) …

Data privacy is becoming increasingly important as data becomes more valuable, as
evidenced by the enactment of right-to-be-forgotten laws and regulations. However, in a …

The increasing concerns regarding the privacy of machine learning models have catalyzed
the exploration of machine unlearning, ie, a process that removes the influence of training …