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 …
M Chen, W Gao, G Liu, K Peng… - Proceedings of the …, 2023 - openaccess.thecvf.com
The practical needs of the" right to be forgotten" and poisoned data removal call for efficient machine unlearning techniques, which enable machine learning models to unlearn, or to …
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 increasing data privacy concerns in recommendation systems have made federated recommendations attract more and more attention. Existing federated recommendation …
Over the past decades, the abundance of personal data has led to the rapid development of machine learning models and important advances in artificial intelligence (AI). However …
J Wang, S Guo, X Xie, H Qi - Proceedings of the ACM Web Conference …, 2022 - dl.acm.org
We explore the problem of selectively forgetting categories from trained CNN classification models in federated learning (FL). Given that the data used for training cannot be accessed …
C Wu, S Zhu, P Mitra - arXiv preprint arXiv:2201.09441, 2022 - arxiv.org
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 …
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 …