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Federated learning enables model training over a distributed corpus of agent data.
Network in network, 2014
M. Lin, Q. Chen, and S. Yan · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Federated learning: Strategies for improving communication efficiency
J. Konečný, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2016
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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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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Emnist: an extension of mnist to handwritten letters, 2017
G. Cohen, S. Afshar, J. Tapson, and A. van Schaik · 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, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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A hybrid approach to privacy-preserving federated learning
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou · 2019
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Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd
J. Wang and G. Joshi · 2019
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On the convergence and robustness of adversarial training
Y. Wang, X. Ma, J. Bailey, J. Yi, B. Zhou, and Q. Gu · 2019
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H. Yu, R. Jin, and S. Yang · 2019
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Bayesian nonparametric federated learning of neural networks
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni · 2019
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H. Yu, S. Yang, and S. Zhu · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
J. Cohen, E. Rosenfeld, and Z. Kolter · 2019
Cited alongside, same era.
Communication trade-offs for local-sgd with large step size
A. Dieuleveut and K. K. Patel · 2019
Cited alongside, same era.
Using pre-training can improve model robustness and uncertainty
D. Hendrycks, K. Lee, and M. Mazeika · 2019
Cited alongside, same era.
Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konecný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao · 2019
Cited alongside, same era.
Overcoming forgetting in federated learning on non-iid data
N. Shoham, T. Avidor, A. Keren, N. Israel, D. Benditkis, L. Mor-Yosef, and I. Zeitak · 2019
Cited alongside, same era.
Cifar-10 (canadian institute for advanced research)
A. Krizhevsky, V. Nair, and G. Hinton
Cited in the paper.
Improving the affordability of robustness training for dnns
S. Gupta, P. Dube, and A. Verma · 2020
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Local sgd with a communication overhead depending only on the number of workers
A. Spiridonoff, A. Olshevsky, and I. C. Paschalidis · 2020
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Federated learning with matched averaging
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni · 2020
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Federated continual learning with weighted inter-client transfer
J. Yoon, W. Jeong, G. Lee, E. Yang, and S. J. Hwang · 2020
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Fat: Federated adversarial training
G. Zizzo, A. Rawat, M. Sinn, and B. Buesser · 2020
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