A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
Author:
Affiliation:
1. Nanyang Technological University, Singapore, Singapore
2. Nanyang Technological University, Singapore Singapore
3. The University of Melbourne, Melbourne, Australia
4. RMIT University, Melbourne, Australia
Abstract
Publisher
Association for Computing Machinery (ACM)
Link
https://dl.acm.org/doi/pdf/10.1145/3679014
Reference159 articles.
1. Manaar Alam Hithem Lamri and Michail Maniatakos. 2023. Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning. arXiv preprint arXiv:2304.10638(2023).
2. Eugene Bagdasaryan and Vitaly Shmatikov. 2021. Blind backdoors in deep learning models. In 30th USENIX Security Symposium (USENIX Security 21). 1505–1521.
3. Hasin Bano, Muhammad Ameen, Muntazir Mehdi, Amaad Hussain, and Pengfei Wang. 2023. Federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions. In International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer, 393–410.
4. Secure Single-Server Aggregation with (Poly)Logarithmic Overhead
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