Learn to Unlearn: Insights Into Machine Unlearning
Author:
Affiliation:
1. Data61, Commonwealth Scientific and Industrial Research Organization, Burwood, VIC, Australia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/2/10461644/10461690.pdf?arnumber=10461690
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Grouped Federated Meta-Learning for Privacy-Preserving Rare Disease Diagnosis;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
2. From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
3. Machine Unlearning: Solutions and Challenges;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-06
4. Why the generative AI models do not like the right to be forgotten: a study of proportionality of identified limitations;Przegląd Prawniczy Uniwersytetu im. Adam Mickiewicza;2023-12-30
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