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 …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

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 evolution of privacy-preserving Federated Learning (FL) has led to an increasing
demand for implementing the right to be forgotten. The implementation of selective forgetting …

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 unlearning (FUL) is a promising solution for removing negative influences from
the global model. However, ensuring the reliability of local models in FL systems remains …

Federated unlearning (FU) algorithms offer participants in federated learning (FL) the" right
to be forgotten''for their individual data and its impact on a collaboratively trained model …

Federated learning (FL) offers a credible solution for distributed data trading since it could
train machine learning models in a distributed manner thereby enhancing data privacy …

The right to be forgotten, as stated in most data regulations, poses an underexplored
challenge in federated learning (FL), leading to the development of federated unlearning …

Federated learning (FL) has enabled collaborative model training across decentralized data
sources or clients. While adding new participants to a shared model does not pose great …