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 …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

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 …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

Federated Learning (FL), the de-facto distributed machine learning paradigm that locally
trains datasets at individual devices, is vulnerable to backdoor model poisoning attacks. By …

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 …

Machine unlearning aims to remove points from the training dataset of a machine learning
model after training; for example when a user requests their data to be deleted. While many …

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service
(MLaaS) has been extensively studied with increasing concerns about data privacy. It …

By allowing users to erase their data's impact on federated learning models, federated
unlearning protects users' right to be forgotten and data privacy. Despite a burgeoning body …

The “Right to be Forgotten” rule in machine learning (ML) practice enables some individual
data to be deleted from a trained model, as pursued by recently developed machine …