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 …

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 …

Owing to its practical configuration to edge computing and privacy preservation capabilities,
federated learning (FL) has been increasingly appealing in Internet of Things (IoT) networks …

The Right to be Forgotten gives a data owner the right to revoke their data from an entity
storing it. In the context of federated learning, the Right to be Forgotten requires that, in …

As the right to be forgotten has been legislated worldwide, many studies attempt to design
unlearning mechanisms to protect users' privacy when they want to leave machine learning …

As some recent information security legislation endowed users with unconditional rights to
be forgotten by any trained machine learning model, personalised IoT service providers …

With recent legislation on the right to be forgotten, machine unlearning has emerged as a
crucial research area. It facilitates the removal of a user's data from federated trained …

Federated learning (FL) has recently emerged as a compelling machine learning paradigm,
prioritizing the protection of privacy for training data. The increasing demand to address …

In recent years, Federated Unlearning (FU) has gained attention for addressing the removal
of a client's influence from the global model in Federated Learning (FL) systems, thereby …

Federated unlearning is a promising paradigm for protecting the data ownership of
distributed clients. It allows central servers to remove historical data effects within the …