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Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Stealthy poisoning attacks on pca-based anomaly detectors
B. I. Rubinstein, B. Nelson, L. Huang, A. D. Joseph, S.-h. Lau, S. Rao, N. Taft, and J. Tygar · 2009
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Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and P. Laskov · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2014
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Visualizing and understanding convolutional networks
M. Zeiler and R. Fergus · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Using machine teaching to identify optimal training-set attacks on machine learners
S. Mei and X. Zhu · 2015
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Explaining nonlinear classification decisions with deep taylor decomposition
G. Montavon, S. Bach, A. Binder, W. Samek, and K. Müller · 2015
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Is feature selection secure against training data poisoning?
H. Xiao, B. Biggio, G. Brown, G. Fumera, C. Eckert, and F. Roli · 2015
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Machine learning with adversaries: Byzantine tolerant gradient descent
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
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Towards poisoning of deep learning algorithms with back-gradient optimization
L. Muñoz-González, B. Biggio, A. Demontis, A. Paudice, V. Wongrassamee, E. C. Lupu, and F. Roli · 2017
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Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. B. Viégas, and M. Wattenberg · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Y. Chen, L. Su, and J. Xu · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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Trojaning attack on neural networks
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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How to backdoor federated learning
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov · 2018
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DRACO: byzantine-resilient distributed training via redundant gradients
L. Chen, H. Wang, Z. B. Charles, and D. S. Papailiopoulos · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li · 2018
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The hidden vulnerability of distributed learning in byzantium
E. M. E. Mhamdi, R. Guerraoui, and S. Rouault · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett · 2018
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