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The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems.
G. Liu, X. Ma, Y. Yang, C. Wang, and J. Liu, “FedEraser: Enabling efficient client-level data removal from federated learning models,” in 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)
2021
Earlier work this paper cites.
L. Wu, S. Guo, J. Wang, Z. Hong, J. Zhang, and Y. Ding, “Federated unlearning: Guarantee the right of clients to forget,” IEEE Network
2022
Earlier work this paper cites.
J. Wang, S. Guo, X. Xie, and H. Qi, “Federated unlearning via class-discriminative pruning,” in Proceedings of the ACM Web Conference 2022
2022
Earlier work this paper cites.
F. Wang, B. Li, and B. Li, “Federated unlearning and its privacy threats,” IEEE Network
2023
Earlier work this paper cites.
N. Su and B. Li, “Asynchronous federated unlearning,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
2023
Earlier work this paper cites.
W. Yuan, H. Yin, F. Wu, S. Zhang, T. He, and H. Wang, “Federated unlearning for on-device recommendation,” in Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
2023
Cited alongside, same era.
X. Zhu, G. Li, and W. Hu, “Heterogeneous federated knowledge graph embedding learning and unlearning,” in Proceedings of the ACM Web Conference 2023
2023
Cited alongside, same era.
W. Wang, Z. Tian, C. Zhang, A. Liu, and S. Yu, “BFU: Bayesian federated unlearning with parameter self-sharing,” in Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
2023
Cited alongside, same era.
H. Xia, S. Xu, J. Pei, R. Zhang, Z. Yu, W. Zou, L. Wang, and C. Liu, “FedME 2 : Memory evaluation & \& erase promoting federated unlearning in DTMN,” IEEE Journal on Selected Areas in Communications
2023
Cited alongside, same era.
Z. Xiong, W. Li, Y. Li, and Z. Cai, “Exact-Fun: An exact and efficient federated unlearning approach,” in 2023 IEEE International Conference on Data Mining (ICDM)
2023
Closest in time.
T. Che, Y. Zhou, Z. Zhang, L. Lyu, J. Liu, D. Yan, D. Dou, and J. Huan, “Fast federated machine unlearning with nonlinear functional theory,” in Proceedings of the 40th International Conference on Machine Learning
2023
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P. Wang, Z. Yan, M. S. Obaidat, Z. Yuan, L. Yang, J. Zhang, Z. Wei, and Q. Zhang, “Edge caching with federated unlearning for low-latency v2x communications,” IEEE Communications Magazine
2023
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X. Guo, P. Wang, S. Qiu, W. Song, Q. Zhang, X. Wei, and D. Zhou, “FAST: Adopting federated unlearning to eliminating malicious terminals at server side,” IEEE Transactions on Network Science and Engineering
2024
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L. Zhang, T. Zhu, H. Zhang, P. Xiong, and W. Zhou, “FedRecovery: Differentially private machine unlearning for federated learning frameworks,” IEEE Transactions on Information Forensics and Security
2023
Cited alongside, same era.
Y. Zhao, P. Wang, H. Qi, J. Huang, Z. Wei, and Q. Zhang, “Federated unlearning with momentum degradation,” IEEE Internet of Things Journal
2024
Closest in time.