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Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, Alex, Hinton, Geoffrey, et al · 2009
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The German Traffic Sign Recognition Benchmark: A multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Detecting cancer metastases on gigapixel pathology images
Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E. Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q. Nelson, Gregory S. Corrado, Jason D. Hipp, Lily Peng, and Martin C. Stumpe · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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The california consumer privacy act: towards a european-style privacy regime in the united states
Stuart L Pardau · 2018
Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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The seven sins of personal-data processing systems under GDPR
Supreeth Shastri, Melissa Wasserman, and Vijay Chidambaram · 2019
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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DBA: distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 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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Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin B. Calo · 2019
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Lifelong anomaly detection through unlearning
Min Du, Zhi Chen, Chang Liu, Rajvardhan Oak, and Dawn Song · 2019
Cited alongside, same era.
Making AI forget you: Data deletion in machine learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant, and James Zou · 2019
Cited alongside, same era.
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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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