Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Recently, an increasing number of laws have governed the useability of users' privacy. For
example, Article 17 of the General Data Protection Regulation (GDPR), the right to be …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

In recent years, the notion of``the right to be forgotten"(RTBF) has evolved into a
fundamental element of data privacy regulations, affording individuals the ability to request …

Abstract We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large
models on a diverse collection of data while minimizing the expected cost to remove the …

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately
reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual …

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 …

Vertical federated learning is designed to protect user privacy by building local models over
disparate datasets and transferring intermediate parameters without directly revealing the …

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