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 (FU) is gaining prominence for its capacity to eliminate influences of
Federated Learning (FL) users' data from trained global FL models. A straightforward FU …

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

With privacy legislation empowering users with the right to be forgotten, it has become
essential to make a model forget about some of its training data. We explore the problem of …

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 …

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) 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 is a promising privacy-preserving paradigm for distributed machine
learning. In this context, there is sometimes a need for a specialized process called machine …

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 …