Abstract Machine unlearning is the problem of removing the effect of a subset of training
data (the``forget set'') from a trained model without damaging the model's utility eg to comply …

Machine unlearning aims to efficiently eliminate the influence of specific training data,
known as the forget set, from the model. However, existing unlearning methods for Large …

Federated Unlearning (FU) aims to delete specific training data from an ML model trained
using Federated Learning (FL). However, existing FU methods suffer from inefficiencies due …

In this work, we address the problem of large language model (LLM) unlearning, aiming to
remove unwanted data influences and associated model capabilities (eg, copyrighted data …

Machine unlearning (MUL) focuses on removing the influence of specific subsets of data
(such as noisy, poisoned, or privacy-sensitive data) from pretrained models. MUL methods …

Machine unlearning refers to removing the influence of a specified subset of training data
from a machine learning model, efficiently, after it has already been trained. This is important …

Machine unlearning aims to solve the problem of removing the influence of selected training
examples from a learned model. Despite the increasing attention to this problem, it remains …

Supply chains are dynamic systems with constantly changing data, necessitating adaptive
machine learning models. While prior research emphasizes integrating new data to …