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Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other.
Estimating a Dirichlet distribution
T. Minka · 2000
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A. Krizhevsky · 2009
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Antidote: Understanding and defending against poisoning of anomaly detectors
B. Rubinstein, B. Nelson, L. Huang, A. D. Joseph, S.-h. Lau, S. Rao, N. Taft, and J. D. Tygar · 2009
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Adversarial machine learning
L. Huang, A. D. Joseph, B. Nelson, B. Rubinstein, and J. Tygar · 2011
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Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and P. Laskov · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, and Y. Bengio · 2013
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Experimental evidence of massive-scale emotional contagion through social networks
A. D. Kramer, J. E. Guillory, and J. T. Hancock · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Revisiting distributed synchronous SGD
J. Chen, R. Monga, S. Bengio, and R. Jozefowicz · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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Auror: Defending against poisoning attacks in collaborative deep learning systems
S. Shen, S. Tople, and P. Saxena · 2016
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Making sense of recommendations
M. Yeomans, A. K. Shah, S. Mullainathan, and J. Kleinberg · 2016
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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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Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 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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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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S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, G. Smith, and B. Thorne · 2017
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Tying word vectors and word classifiers: A loss framework for language modeling
H. Inan, K. Khosravi, and R. Socher · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 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
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas · 2017
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SecureML: A system for scalable privacy-preserving machine learning
P. Mohassel and Y. Zhang · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Multi-party poisoning through generalized p p -tampering
S. Mahloujifar, M. Mahmoody, and A. Mohammed · 2018
Closest in time.
Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
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Variational continual learning
C. V. Nguyen, Y. Li, T. D. Bui, and R. E. Turner · 2018
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Scalable private learning with PATE
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson · 2018
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Biscotti: A ledger for private and secure peer-to-peer machine learning
M. Shayan, C. Fung, C. J. Yoon, and I. Beschastnikh · 2018
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Spectral signatures in backdoor attacks
B. Tran, J. Li, and A. Madry · 2018
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Cited alongside, same era.
Using the output embedding to improve language models
O. Press and L. Wolf · 2017
Cited alongside, same era.
Learning discrete distributions from untrusted batches
M. Qiao and G. Valiant · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Certified defenses for data poisoning attacks
J. Steinhardt, P. W. Koh, and P. S. Liang · 2017
Cited alongside, same era.
Learning spread-out local feature descriptors
X. Zhang, X. Y. Felix, S. Kumar, and S.-F. Chang · 2017
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
A. N. Bhagoji, S. Chakraborty, P. Mittal, and S. Calo · 2018
Cited alongside, same era.
Closest in time.
Clean-label backdoor attacks
A. Turner, D. Tsipras, and A. Madry · 2018
Closest in time.
Generalized byzantine-tolerant SGD
C. Xie, O. Koyejo, and I. Gupta · 2018
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Zeno: Byzantine-suspicious stochastic gradient descent
C. Xie, O. Koyejo, and I. Gupta · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
D. Yin, Y. Chen, R. Kannan, and P. Bartlett · 2018
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PoTrojan: Powerful neural-level trojan designs in deep learning models
M. Zou, Y. Shi, C. Wang, F. Li, W. Song, and Y. Wang · 2018
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A little is enough: Circumventing defenses for distributed learning
M. Baruch, G. Baruch, and Y. Goldberg · 2019
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Towards federated learning at scale: System design
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H. B. McMahan, T. Van Overveldt, D. Petrou, D. Ramage, and J. Roselander · 2019
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DeepInspect: A black-box trojan detection and mitigation framework for deep neural networks
H. Chen, C. Fu, J. Zhao, and F. Koushanfar · 2019
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Distributed training with heterogeneous data: Bridging median and mean based algorithms
X. Chen, T. Chen, H. Sun, Z. S. Wu, and M. Hong · 2019
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https://decentralizedml.com/ , 2019
Decentralized ML · 2019
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Strip: A defence against trojan attacks on deep neural networks
Y. Gao, C. Xu, D. Wang, S. Chen, D. C. Ranasinghe, and S. Nepal · 2019
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Under the hood of the Pixel 2: How AI is supercharging hardware
Google · 2019
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Exploiting unintended feature leakage in collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2019
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Comprehensive privacy analysis of deep learning: Stand-alone and federated learning under passive and active white-box inference attacks
M. Nasr, R. Shokri, and A. Houmansadr · 2019
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https://www.openmined.org/ , 2019
OpenMined · 2019
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https://github.com/pytorch/examples/tree/master/word_language_model/ , 2019
PyTorch examples · 2019
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Bypassing backdoor detection algorithms in deep learning
T. J. L. Tan and R. Shokri · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
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