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Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates.
Towards Federated Learning at Scale: System Design
Kallista Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 1902
Earlier work this paper cites.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 1911
Earlier work this paper cites.
Can You Really Backdoor Federated Learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H. Brendan McMahan · 1911
Earlier work this paper cites.
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, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 1912
Earlier work this paper cites.
Deep Model Poisoning Attack on Federated Learning
Xingchen Zhou, Ming Xu, Yiming Wu, and Ning Zheng · 1999
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Blind Backdoors in Deep Learning Models
Eugene Bagdasaryan and Vitaly Shmatikov · 2005
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The security of machine learning
Marco Barreno, Blaine Nelson, Anthony D. Joseph, and J. D. Tygar · 2010
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Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein · 2012
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Federated Optimization:Distributed Optimization Beyond the Datacenter
Jakub Konečný, Brendan McMahan, and Daniel Ramage · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Practical Secure Aggregation for Privacy Preserving Machine Learning
Kallista Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C. Lupu, and Fabio Roli · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Battista Biggio and Fabio Roli · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning, July 2020
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
Later among the works it cites.
Covert Channel Attack to Federated Learning Systems
Gabriele Costa, Fabio Pinelli, Simone Soderi, and Gabriele Tolomei · 2021
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Widen The Backdoor To Let More Attackers In, October 2021
Siddhartha Datta, Giulio Lovisotto, Ivan Martinovic, and Nigel Shadbolt · 2021
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functorch: Jax-like composable function transforms for pytorch
Horace He and Richard Zou · 2021
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Fedbn: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
Cited alongside, same era.
How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2019
Cited alongside, same era.
A Little Is Enough: Circumventing Defenses For Distributed Learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg · 2019
Cited alongside, same era.
Analyzing Federated Learning through an Adversarial Lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Cited alongside, same era.
Federated Learning for Mobile Keyboard Prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
Cited alongside, same era.
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu · 2021
Later among the works it cites.
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, Sudeep Agarwal, Julien Freudiger, Andrew Byde, Abhishek Bhowmick, Gaurav Kapoor, Si Beaumont, Áine Cahill, Dominic Hughes, Omid Javidbakht, Fei Dong, Rehan Rishi, and Stanley Hung · 2021
Later among the works it cites.
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage · 2021
Later among the works it cites.
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon, Werner Zellinger, Bernhard A. Moser, Alina Oprea, Battista Biggio, Marcello Pelillo, and Fabio Roli · 2022
Closest in time.
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal Diaz, Andre Manoel, and Robert Sim · 2022
Closest in time.
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures
Huming Qiu, Hua Ma, Zhi Zhang, Alsharif Abuadbba, Wei Kang, Anmin Fu, and Yansong Gao · 2022
Closest in time.
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
Yuwei Sun, Hideya Ochiai, and Jun Sakuma · 2022
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Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey
Zhilin Wang, Qiao Kang, Xinyi Zhang, and Qin Hu · 2022
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Backdoor Attacks in Federated Learning by Poisoned Word Embeddings
KiYoon Yoo and Nojun Kwak · 2022
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Neurotoxin: durable backdoors in federated learning
Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael Mahoney, Prateek Mittal, Ramchandran Kannan, and Joseph Gonzalez · 2022
Closest in time.