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Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A survey of outlier detection methodologies
Victoria Hodge and Jim Austin · 2004
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Casting out demons: Sanitizing training data for anomaly sensors
Gabriela F Cretu, Angelos Stavrou, Michael E Locasto, Salvatore J Stolfo, and Angelos D Keromytis · 2008
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Antidote: understanding and defending against poisoning of anomaly detectors
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Efficient large-scale distributed training of conditional maximum entropy models
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Learning multiple layers of features from tiny images
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Parallelized stochastic gradient descent
Martin Zinkevich, Markus Weimer, Lihong Li, and Alex J Smola · 2010
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Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and J Doug Tygar · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Machine teaching for bayesian learners in the exponential family
Jerry Zhu · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Distributed stochastic optimization and learning
Ohad Shamir and Nathan Srebro · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Using machine teaching to identify optimal training-set attacks on machine learners
Shike Mei and Xiaojin Zhu · 2015
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Data poisoning attacks against autoregressive models
Scott Alfeld, Xiaojin Zhu, and Paul Barford · 2016
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Robust multi-agent optimization: Coping with byzantine agents with input redundancy
Lili Su and Nitin H. Vaidya · 2016
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Non-bayesian learning in the presence of byzantine agents
Lili Su and Nitin H. Vaidya · 2016
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Parallel sgd: When does averaging help?
Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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An on-device deep neural network for face detection
Computer Vision Machine Learning Team (Apple) · 2017
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The iphone x’s new neural engine exemplifies apple’s approach to ai
James Vincent · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, Rachid Guerraoui, Julien Stainer, et al · 2017
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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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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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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Some submodular data-poisoning attacks on machine learners
Shike Mei and Xiaojin Zhu · 2017
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John C. Duchi · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Federated learning of n-gram language models
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, and Michael Riley · 2019
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Personalization of end-to-end speech recognition on mobile devices for named entities
Khe Chai Sim, Francoise Beaufays, Arnaud Benard, Dhruv Guliani, Andreas Kabel, Nikhil Khare, Tamar Lucassen, Petr Zadrazil, Harry Zhang, Leif Johnson, and et al · 2019
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Yudong Chen, Lili Su, and Jiaming Xu · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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From parity to preference-based notions of fairness in classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, and Adrian Weller · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Federated learning for healthcare informatics
Jie Xu and Fei Wang · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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The byzantine generals problem
Leslie Lamport, Robert Shostak, and Marshall Pease · 2019
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Practical distributed learning: Secure machine learning with communication-efficient local updates
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Zeno++: robust asynchronous sgd with arbitrary number of byzantine workers
Cong Xie · 2019
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A little is enough: Circumventing defenses for distributed learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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Tesla didn’t fix an autopilot problem for three years, and now another person is dead
Andrew Hawkins · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
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Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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Defending against saddle point attack in byzantine-robust distributed learning
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2019
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Aggregathor: Byzantine machine learning via robust gradient aggregation
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Hany Abdelmessih Guirguis, and Sébastien Louis Alexandre Rouault · 2019
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Election coding for distributed learning: Protecting signsgd against byzantine attacks
Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, and Jaekyun Moon · 2019
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Detox: A redundancy-based framework for faster and more robust gradient aggregation
Shashank Rajput, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2019
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Ardis: a swedish historical handwritten digit dataset
Huseyin Kusetogullari, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn, and Johan Hall · 2019
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
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The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B Gibbons · 2019
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A federated learning framework for healthcare iot devices
Binhang Yuan, Song Ge, and Wenhui Xing · 2020
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The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett Landman, Klaus Maier-Hein, et al · 2020
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Fednas: Federated deep learning via neural architecture search
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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