Federated unlearning (FUL) is a promising solution for removing negative influences from
the global model. However, ensuring the reliability of local models in FL systems remains …

Unlearning the data observed during the training of a machine learning (ML) model is an
important task that can play a pivotal role in fortifying the privacy and security of ML-based …

Machine unlearning is a process of removing the impact of some training data from the
machine learning (ML) models upon receiving removal requests. While straightforward and …

Machine unlearning is an emerging need that aims to remove the influence of deleted data
from a learned model in a timely manner. Thus, unlearning is important for privacy and …

Software systems that learn from user data with machine learning (ML) have become
ubiquitous over the last years. Recent law such as the" General Data Protection …

With growing concerns regarding privacy in machine learning models, regulations have
committed to granting individuals the right to be forgotten while mandating companies to …

ML applications proliferate across various sectors. Large internet firms employ ML to train
intelligent models using vast datasets, including sensitive user information. However, new …

This article presents a comprehensive review of recent machine unlearning techniques,
verification mechanisms, and potential attacks. We highlight emerging challenges and …

Modern privacy regulations grant citizens the right to be forgotten by products, services and
companies. In case of machine learning (ML) applications, this necessitates deletion of data …

5G networks with the vast number of devices pose security threats. Manual analysis of such
extensive security data is complex. Dark-NMF can detect malware activities by monitoring …