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 …

SoK: Challenges and Opportunities in Federated Unlearning

H Jeong, S Ma, A Houmansadr - arXiv preprint arXiv:2403.02437, 2024 - arxiv.org
Federated learning (FL), introduced in 2017, facilitates collaborative learning between non-
trusting parties with no need for the parties to explicitly share their data among themselves …