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 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 …

Bayesian Federated Learning (FL) offers a principled framework to account for the
uncertainty caused by limitations in the data available at the nodes implementing …

Federated learning (FL) is a distributed privacy-preserving paradigm of machine learning
that enables efficient and secure model training through the collaboration of multiple clients …

Federated learning (FL) aims at optimizing a shared global model over multiple edge
devices without transmitting (private) data to the central server. While it is theoretically well …

Recently, the enactment of" right to be forgotten" laws and regulations has imposed new
privacy requirements on federated learning (FL). Researchers aim to remove the influence …