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) 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 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) has emerged as a privacy-aware collaborative learning paradigm
where participants jointly train a powerful model without sharing their private data. One …

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 …

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 …

Federated learning (FL) enables multiple clients to train a machine learning model
collaboratively without exchanging their local data. Federated unlearning is an inverse FL …

Federated learning (FL) has recently emerged as a promising distributed machine learning
(ML) paradigm. Practical needs of the" right to be forgotten" and countering data poisoning …

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 …