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Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Variational gaussian process classifiers
Mark N Gibbs and David JC MacKay · 2000
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Bitcoin: A peer-to-peer electronic cash system
Satoshi Nakamoto · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Delegated proof of stake, 2014
D. Larimer · 2014
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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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The EU general data protection regulation (GDPR)
Paul Voigt and Axel Von dem Bussche · 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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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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When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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Proof of learning (pole): Empowering machine learning with consensus building on blockchains
Yixiao Lan, Yuan Liu, Boyang Li, and Chunyan Miao · 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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Fast-convergent federated learning with adaptive weighting
Hongda Wu and Ping Wang · 2021
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A fast blockchain-based federated learning framework with compressed communications
Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
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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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FedRLChain: Secure federated deep reinforcement learning with blockchain
Sujit Chowdhury, Arnab Mukherjee, and Raju Halder · 2023
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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2023
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DRL-based Adaptive Sharding for Blockchain-based Federated Learning
Yijing Lin, Zhipeng Gao, Hongyang Du, Jiawen Kang, Dusit Niyato, Qian Wang, Jingqing Ruan, and Shaohua Wan · 2023
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Machine unlearning of federated clusters
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Laizhong Cui, Xiaoxin Su, and Yipeng Zhou · 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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A novel architecture combining oracle with decentralized learning for iiot
Yijing Lin, Zhipeng Gao, Weisong Shi, Qian Wang, Huangqi Li, Miaomiao Wang, Yang Yang, and Lanlan Rui · 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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Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, and Olgica Milenkovic · 2023
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Asynchronous federated unlearning
Ningxin Su and Baochun Li · 2023
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Federated unlearning and its privacy threats
Fei Wang, Baochun Li, and Bo Li · 2023
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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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