Machine unlearning is the process through which a deployed machine learning model is
made to forget about some of its training data points. While naively retraining the model from …

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 Unlearning (FU) aims to delete specific training data from an ML model trained
using Federated Learning (FL). We introduce QuickDrop, an efficient and original 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 …

Machine unlearning has become a pivotal task to erase the influence of data from a trained
model. It adheres to recent data regulation standards and enhances the privacy and security …

Machine unlearning enables data owners to remove the contribution of their specified
samples from trained models. However, existing methods fail to strike an optimal balance …

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

Data privacy is becoming increasingly important as data becomes more valuable, as
evidenced by the enactment of right-to-be-forgotten laws and regulations. However, in a …

As a new paradigm to erase data from a model and protect user privacy, machine
unlearning has drawn significant attention. However, existing studies on machine …

As the use of machine learning (ML) models is becoming increasingly popular in many real-
world applications, there are practical challenges that need to be addressed for model …