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Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models.
Term-weighting approaches in automatic text retrieval
Gerard Salton and Christopher Buckley · 1988
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Introduction to coding theory
Ron M Roth · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Polyshard: Coded sharding achieves linearly scaling efficiency and security simultaneously
Songze Li, Mingchao Yu, Chien-Sheng Yang, Amir Salman Avestimehr, Sreeram Kannan, and Pramod Viswanath · 2020
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Optimizing federated learning on non-iid data with reinforcement learning
Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li · 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
Cited alongside, same era.
Federaser: Enabling efficient client-level data removal from federated learning models
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2021
Cited alongside, same era.
Knowledge unlearning for mitigating privacy risks in language models
Federated unlearning: Guarantee the right of clients to forget
Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Yaohong Ding · 2022
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Arcane: An efficient architecture for exact machine unlearning
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin · 2022
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, and Eleni Triantafillou · 2023
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Asynchronous federated unlearning
Ningxin Su and Baochun Li · 2023
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Bfu: Bayesian federated unlearning with parameter self-sharing
Weiqi Wang, Zhiyi Tian, Chenhan Zhang, An Liu, and Shui Yu · 2023
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Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2022
Cited alongside, same era.
The right to be forgotten in federated learning: An efficient realization with rapid retraining
Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li · 2022
Cited alongside, same era.
Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
Cited alongside, same era.
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Machine unlearning: A survey
Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, and Philip S Yu · 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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