In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …

The Right to be Forgotten gives a data owner the right to revoke their data from an entity
storing it. In the context of federated learning, the Right to be Forgotten requires that, in …

As the right to be forgotten has been legislated worldwide, many studies attempt to design
machine unlearning mechanisms to enable data erasure from a trained model. Existing …

In federated learning, a strong global model is collaboratively learned by aggregating
clients' locally trained models. Although this precludes the need to access clients' data …

Thanks to regulatory policies such as the General Data Protection Regulation (GDPR), it is
essential to provide users with the right to erasure regarding their own private data, even if …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated learning enables distributed learning in a privacy-protected manner, but two
challenging reasons can affect learning performance significantly. First, mobile users are not …

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 …

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 …

Classical federated learning (FL) enables training machine learning models without sharing
data for privacy preservation, but heterogeneous data characteristic degrades the …