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Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users' data from trained global FL models.
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X. Cao, J. Jia, Z. Zhang, and N. Z. Gong, “FedRecover: Recovering from poisoning attacks in federated learning using historical information,” in IEEE Symposium on Security and Privacy (SP) , 2023, pp. 1366–1383
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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 , 2023
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2023
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2023
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W. Su, P. Wang, Z. Wang, A. Muhammad, T. Tao, X. Tong, and Q. Zhang, “F2ul: Fairness-aware federated unlearning for data trading,” IEEE Transactions on Mobile Computing , 2024
2024
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2024
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2024
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C. Zhao, W. Qian, R. Ying, and M. Huai, “Static and sequential malicious attacks in the context of selective forgetting,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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