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Federated Learning is a promising paradigm for privacy-preserving collaborative model training.
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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Humans forget, machines remember: Artificial intelligence and the right to be forgotten
Eduard Fosch Villaronga, Peter Kieseberg, and Tiffany Li · 2018
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Class clown: Data redaction in machine unlearning at enterprise scale, 2020
Daniel L. Felps, Amelia D. Schwickerath, Joyce D. Williams, Trung N. Vuong, Alan Briggs, Matthew Hunt, Evan Sakmar, David D. Saranchak, and Tyler Shumaker · 2020
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Formalizing data deletion in the context of the right to be forgotten
Sanjam Garg, Shafi Goldwasser, and Prashant Nalini Vasudevan · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Dmcp: Differentiable markov channel pruning for neural networks
Shaopeng Guo, Yujie Wang, Quanquan Li, and Junjie Yan · 2020
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Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2020
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Genseg and mr-genseg: A novel segmentation algorithm and its parallel mapreduce based approach for identifying genomic regions with copy number variations
Rituparna Sinha, Rajat K Pal, and Rajat K De · 2020
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Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 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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Machine unlearning for random forests
Jonathan Brophy and Daniel Lowd · 2021
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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A network embedding-enhanced bayesian model for generalized community detection in complex networks
Dongxiao He, Youyou Wang, Jinxin Cao, Weiping Ding, Shizhan Chen, Zhiyong Feng, Bo Wang, and Yuxiao Huang · 2021
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 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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Multiple kernel clustering with kernel k-means coupled graph tensor learning
Zhenwen Ren, Quansen Sun, and Dong Wei · 2021
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Hedgecut: Maintaining randomised trees for low-latency machine unlearning
Sebastian Schelter, Stefan Grafberger, and Ted Dunning · 2021
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Machine unlearning: Linear filtration for logit-based classifiers
Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer · 2022
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Recommendation unlearning
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding · 2022
Asynchronous federated unlearning
Ningxin Su and Baochun Li · 2023
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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 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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Machine unlearning of features and labels, 2023
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 2023
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Exact-fun: An exact and efficient federated unlearning approach
Zuobin Xiong, Wei Li, Yingshu Li, and Zhipeng Cai · 2023
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A review on machine unlearning
Haibo Zhang, Toru Nakamura, Takamasa Isohara, and Kouichi Sakurai · 2023
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Graph unlearning
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2022
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Federated unlearning: How to efficiently erase a client in fl?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, and Nathalie Baracaldo · 2022
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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
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A survey of machine unlearning, 2022
Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022
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Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
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Machine unlearning for image retrieval: A generative scrubbing approach
Peng-Fei Zhang, Guangdong Bai, Zi Huang, and Xin-Shun Xu · 2022
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Machine unlearning in large language models, 2024
Kongyang Chen, Zixin Wang, Bing Mi, Waixi Liu, Shaowei Wang, Xiaojun Ren, and Jiaxing Shen · 2024
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Towards scalable exact machine unlearning using parameter-efficient fine-tuning
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New york times sues openai and microsoft, 2023
CNN · 2024
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Unlearning during learning: An efficient federated machine unlearning method, 2024
Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao, Yuxing Han, Lixin Fan, and Qiang Yang · 2024
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Cifar-10 and cifar-100 datasets
Alex Krizhevsky · 2024
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Fast-ntk: Parameter-efficient unlearning for large-scale models
Guihong Li, Hsiang Hsu, Chun-Fu Chen, and Radu Marculescu · 2024
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Rethinking machine unlearning for large language models, 2024
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, and Yang Liu · 2024
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vision-transformers-cifar10: Training vision transformers (vit) and related models on cifar-10
Kentaro Yoshioka · 2024
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