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Federated unlearning (FU) aims to remove a participant's data contributions from a trained federated learning (FL) model, ensuring privacy and regulatory compliance.
Asymptotic theory for principal component analysis
Theodore Wilbur Anderson · 1963
Earlier work this paper cites.
A limit theorem for the norm of random matrices
Stuart Geman · 1980
Earlier work this paper cites.
On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
Yong-Qua Yin, Zhi-Dong Bai, and Pathak R Krishnaiah · 1988
Earlier work this paper cites.
Information geometry
SI Amari · 1997
Earlier work this paper cites.
The vanishing gradient problem during learning recurrent neural nets and problem solutions
Sepp Hochreiter · 1998
Earlier work this paper cites.
Natural gradient descent for on-line learning
Magnus Rattray, David Saad, and Shun-ichi Amari · 1998
Earlier work this paper cites.
An optimal vq codebook design using the co-adaptation of learning and evolution
Daijin Kim and Sunha Ahn · 2000
Earlier work this paper cites.
Tracy–Widom limit for the largest eigenvalue of a large class of complex sample covariance matrices
Noureddine El Karoui · 2007
Earlier work this paper cites.
Spectrum estimation for large dimensional covariance matrices using random matrix theory
Noureddine El Karoui · 2008
Earlier work this paper cites.
The difficulty of training deep architectures and the effect of unsupervised pre-training
Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, and Pascal Vincent · 2009
Earlier work this paper cites.
Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal Vincent · 2010
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Earlier work this paper cites.
A survey of swarm robotics system
Zhiguo Shi, Jun Tu, Qiao Zhang, Lei Liu, and Junming Wei · 2012
Earlier work this paper cites.
Measure theory
Donald L Cohn · 2013
Earlier work this paper cites.
Coevolution
D.J. Futuyma · 2013
Earlier work this paper cites.
A multi-agent control framework for co-adaptation in brain-computer interfaces
Josh S Merel, Roy Fox, Tony Jebara, and Liam Paninski · 2013
Earlier work this paper cites.
Continuous martingales and Brownian motion
Daniel Revuz and Marc Yor · 2013
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
Earlier work this paper cites.
The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, Geoffrey Hinton, et al · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Free probability and random matrices
James A Mingo and Roland Speicher · 2017
Earlier work this paper cites.
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Jeffrey Pennington, Samuel Schoenholz, and Surya Ganguli · 2017
Earlier work this paper cites.
Stochastic gradient descent as approximate bayesian inference
Mandt Stephan, Matthew D Hoffman, David M Blei, et al · 2017
Cited alongside, same era.
Dynamical isometry and a mean field theory of rnns: Gating enables signal propagation in recurrent neural networks
Minmin Chen, Jeffrey Pennington, and Samuel Schoenholz · 2018
Cited alongside, same era.
Approximate fisher information matrix to characterize the training of deep neural networks
Zhibin Liao, Tom Drummond, Ian Reid, and Gustavo Carneiro · 2018
Cited alongside, same era.
Dynamical isometry and a mean field theory of CNNs: How to train 10,000-layer vanilla convolutional neural networks
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel Schoenholz, and Jeffrey Pennington · 2018
Cited alongside, same era.
Initialization of relus for dynamical isometry
Rebekka Burkholz and Alina Dubatovka · 2019
Cited alongside, same era.
Signal propagation in transformers: Theoretical perspectives and the role of rank collapse
Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, and Aurelien Lucchi · 2022
Later among the works it cites.
Unrolling sgd: Understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
Later among the works it cites.
Stnet: Scale tree network with multi-level auxiliator for crowd counting
Mingjie Wang, Hao Cai, Xian-Feng Han, Jun Zhou, and Minglun Gong · 2022
Later among the works it cites.
Federated unlearning with knowledge distillation
Chen Wu, Sencun Zhu, and Prasenjit Mitra · 2022
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Federated learning for the internet of things: Applications, challenges, and opportunities
Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, and A Salman Avestimehr · 2022
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Badnets: Identifying vulnerabilities in the machine learning model supply chain, 2019
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
Cited alongside, same era.
Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
Cited alongside, same era.
Universal statistics of fisher information in deep neural networks: Mean field approach
Ryo Karakida, Shotaro Akaho, and Shun-ichi Amari · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
Cited alongside, same era.
Adversarial robustness toolbox v1.0.0, 2019
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, and Ben Edwards · 2019
Cited alongside, same era.
Breaking inter-layer co-adaptation by classifier anonymization
Ikuro Sato, Kohta Ishikawa, Guoqing Liu, and Masayuki Tanaka · 2019
Cited alongside, same era.
Dynamical isometry is achieved in residual networks in a universal way for any activation function
Wojciech Tarnowski, Piotr Warchoł, Stanisław Jastrzobski, Jacek Tabor, and Maciej Nowak · 2019
Cited alongside, same era.
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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Complete guide to general data protection regulation compliance
European Union EU · 2023
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Model sparsity can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 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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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 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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Federated unlearning with momentum degradation
Yian Zhao, Pengfei Wang, Heng Qi, Jianguo Huang, Zongzheng Wei, and Qiang Zhang · 2023
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Enable the right to be forgotten with federated client unlearning in medical imaging
Zhipeng Deng, Luyang Luo, and Hao Chen · 2024
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Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation
Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2024
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Fast machine unlearning without retraining through selective synaptic dampening
Jack Foster, Stefan Schoepf, and Alexandra Brintrup · 2024
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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 · 2024
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Dfml: Decentralized federated mutual learning
Yasser H Khalil, Amir H Estiri, Mahdi Beitollahi, Nader Asadi, Sobhan Hemati, Xu Li, Guojun Zhang, and Xi Chen · 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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Federated unlearning with gradient descent and conflict mitigation
Zibin Pan, Zhichao Wang, Chi Li, Kaiyan Zheng, Boqi Wang, Xiaoying Tang, and Junhua Zhao · 2024
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Advances and open challenges in federated learning with foundation models
Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, et al · 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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Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey
Yichen Wan, Youyang Qu, Wei Ni, Yong Xiang, Longxiang Gao, and Ekram Hossain · 2024
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Machine unlearning: Solutions and challenges
Jie Xu, Zihan Wu, Cong Wang, and Xiaohua Jia · 2024
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