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In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby ensuring the ``right to be forgotten" (RTBF).
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Morten Dahl, Chao Ning, and Tomas Toft · 2012
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Yinzhi Cao and Junfeng Yang · 2015
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General Data Protection Regulation · 2018
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Ivan Damgård, Daniel Escudero, Tore Frederiksen, Marcel Keller, Peter Scholl, and Nikolaj Volgushev · 2019
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Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2020
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Lie He, Sai Praneeth Karimireddy, and Martin Jaggi · 2020
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Ziyao Liu, Ivan Tjuawinata, Chaoping Xing, and Kwok-Yan Lam · 2020
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Poseidon: Privacy-preserving federated neural network learning
Sinem Sav, Apostolos Pyrgelis, Juan R Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, Joao Sa Sousa, and Jean-Pierre Hubaux · 2020
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Guowen Xu, Hongwei Li, Yun Zhang, Shengmin Xu, Jianting Ning, and Robert H Deng · 2020
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2021
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When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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Fast federated machine unlearning with nonlinear functional theory
Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, and Jun Huan · 2023
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A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services
Hongsheng Hu, Shuo Wang, Jiamin Chang, Haonan Zhong, Ruoxi Sun, Shuang Hao, Haojin Zhu, and Minhui Xue · 2023
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Federated unlearning via active forgetting
Yuyuan Li, Chaochao Chen, Xiaolin Zheng, and Jiaming Zhang · 2023
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A survey on federated unlearning: Challenges, methods, and future directions
Ziyao Liu, Yu Jiang, Jiyuan Shen, Minyi Peng, Kwok-Yan Lam, and Xingliang Yuan · 2023
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Long-term privacy-preserving aggregation with user-dynamics for federated learning
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Privacy-enhanced federated learning with weighted aggregation
Jiale Guo, Ziyao Liu, Kwok-Yan Lam, Jun Zhao, and Yiqiang Chen · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Federaser: Enabling efficient client-level data removal from federated learning models
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2021
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Revfrf: Enabling cross-domain random forest training with revocable federated learning
Yang Liu, Zhuo Ma, Yilong Yang, Ximeng Liu, Jianfeng Ma, and Kui Ren · 2021
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A linear-time 2-party secure merge protocol
Brett Hemenway Falk, Rohit Nema, and Rafail Ostrovsky · 2022
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Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2022
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Verifi: Towards verifiable federated unlearning
Xiangshan Gao, Xingjun Ma, Jingyi Wang, Youcheng Sun, Bo Li, Shouling Ji, Peng Cheng, and Jiming Chen · 2022
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Ziyao Liu, Hsiao-Ying Lin, and Yamin Liu · 2023
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Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks
Wei Qian, Chenxu Zhao, Wei Le, Meiyi Ma, and Mengdi Huai · 2023
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Unlearning backdoor attacks in federated learning
Chen Wu, Sencun Zhu, and Prasenjit Mitra · 2023
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Fedme 2: Memory evaluation & erase promoting federated unlearning in dtmn
Hui Xia, Shuo Xu, Jiaming Pei, Rui Zhang, Zhi Yu, Weitao Zou, Lukun Wang, and Chao Liu · 2023
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Fedrecovery: Differentially private machine unlearning for federated learning frameworks
Lefeng Zhang, Tianqing Zhu, Haibin Zhang, Ping Xiong, and Wanlei Zhou · 2023
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Fltracer: Accurate poisoning attack provenance in federated learning
Xinyu Zhang, Qingyu Liu, Zhongjie Ba, Yuan Hong, Tianhang Zheng, Feng Lin, Li Lu, and Kui Ren · 2023
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Strategic data revocation in federated unlearning
Ningning Ding, Ermin Wei, and Randall Berry · 2024
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A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services
Hongsheng Hu, Shuo Wang, Jiamin Chang, Haonan Zhong, Ruoxi Sun, Shuang Hao, Haojin Zhu, and Minhui Xue · 2024
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Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning
Hongsheng Hu, Shuo Wang, Tian Dong, and Minhui Xue · 2024
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Towards efficient and certified recovery from poisoning attacks in federated learning
Yu Jiang, Jiyuan Shen, Ziyao Liu, Chee Wei Tan, and Kwok-Yan Lam · 2024
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Backdoor attacks via machine unlearning
Zihao Liu, Tianhao Wang, Mengdi Huai, and Chenglin Miao · 2024
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Dynamic user clustering for efficient and privacy-preserving federated learning
Ziyao Liu, Jiale Guo, Wenzhuo Yang, Jiani Fan, Kwok-Yan Lam, and Jun Zhao · 2024
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Threats, attacks, and defenses in machine unlearning: A survey
Ziyao Liu, Huanyi Ye, Chen Chen, and Kwok-Yan Lam · 2024
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Communication efficient and provable federated unlearning
Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, and Di Wang · 2024
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Static and sequential malicious attacks in the context of selective forgetting
Chenxu Zhao, Wei Qian, Rex Ying, and Mengdi Huai · 2024
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