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Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Regulation (EU) 2016/679 of the European Parliament and of the Council, 2016
European Parliament and Council of the European Union · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Privacy risks of securing machine learning models against adversarial examples
Liwei Song, Reza Shokri, and Prateek Mittal · 2019
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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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Decentralised Learning in Federated Deployment Environments: A System-Level Survey
Paolo Bellavista, Luca Foschini, and Alessio Mora · 2021
Cited alongside, same era.
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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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
Cited alongside, same era.
Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
Cited alongside, same era.
Federated unlearning with knowledge distillation
Chen Wu, Sencun Zhu, and Prasenjit Mitra · 2022
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Get rid of your trail: Remotely erasing backdoors in federated learning
Manaar Alam, Hithem Lamri, and Michail Maniatakos · 2023
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Fedrecover: Recovering from poisoning attacks in federated learning using historical information
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, and Neil Zhenqiang Gong · 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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FAST: Adopting Federated Unlearning to Eliminating Malicious Terminals at Server Side
Xintong Guo, Pengfei Wang, Sen Qiu, Wei Song, Qiang Zhang, Xiaopeng Wei, and Dongsheng Zhou · 2023
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Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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Local-global knowledge distillation in heterogeneous federated learning with non-iid data
Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Hai Jin, Zheng Xu, and Lichao Sun · 2021
Cited alongside, same era.
Federated unlearning: How to efficiently erase a client in fl?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, and Nathalie Baracaldo · 2022
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Multi-level branched regularization for federated learning
Jinkyu Kim, Geeho Kim, and Bohyung Han · 2022
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Preservation of the global knowledge by not-true distillation in federated learning
Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, and Se-Young Yun · 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
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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Federated learning with label-masking distillation
Jianghu Lu, Shikun Li, Kexin Bao, Pengju Wang, Zhenxing Qian, and Shiming Ge · 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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Not all minorities are equal: Empty-class-aware distillation for heterogeneous federated learning
Kuangpu Guo, Yuhe Ding, Jian Liang, Ran He, Zilei Wang, and Tieniu Tan · 2024
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Model sparsity can simplify machine unlearning
Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, PRANAY SHARMA, Sijia Liu, et al · 2024
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Knowledge distillation in federated learning: A practical guide
Alessio Mora, Irene Tenison, Paolo Bellavista, and Irina Rish · 2024
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Federated unlearning: A survey on methods, design guidelines, and evaluation metrics
Nicolò Romandini, Alessio Mora, Carlo Mazzocca, Rebecca Montanari, and Paolo Bellavista · 2024
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Ferrari: federated feature unlearning via optimizing feature sensitivity
Hanlin Gu, WinKent Ong, Chee Seng Chan, and Lixin Fan · 2025
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