SoK: Challenges and Opportunities in Federated Unlearning

[PDF] arxiv.org

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 …
被引用次数：7 相关文章 所有 2 个版本
[PDF] arxiv.org

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 …
相关文章 所有 2 个版本