A survey on federated unlearning: Challenges, methods, and future directions

Z Liu, Y Jiang, J Shen, M Peng, KY Lam… - ACM Computing …, 2023 - dl.acm.org
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 …

Threats, attacks, and defenses in machine unlearning: A survey

Z Liu, H Ye, C Chen, KY Lam - arXiv preprint arXiv:2403.13682, 2024 - arxiv.org
Recently, Machine Unlearning (MU) has gained considerable attention for its potential to
improve AI safety by removing the influence of specific data from trained Machine Learning …

Towards Federated Domain Unlearning: Verification Methodologies and Challenges

K Tam, K Xu, L Li, H Fu - arXiv preprint arXiv:2406.03078, 2024 - arxiv.org
Federated Learning (FL) has evolved as a powerful tool for collaborative model training
across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and …