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Federated learning is a distributed paradigm that aims at training models using samples distributed across multiple users in a network while keeping the samples on users' devices with the aim of efficiency and protecting users privacy.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. (2019) · 1903
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Differentially private learning with adaptive clipping
Thakkar, O., Andrew, G., and McMahan, H. B. (2019) · 1905
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Yu, H., Jin, R., and Yang, S. (2019) · 1905
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On gradient descent ascent for nonconvex-concave minimax problems
Lin, T., Jin, C., and Jordan, M. I. (2019) · 1906
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On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z. (2019c) · 1907
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2019b) · 1908
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Differentially private meta-learning
Li, J., Khodak, M., Caldas, S., and Talwalkar, A. (2019a) · 1909
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., and Pedarsani, R. (2019) · 1909
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On the convergence of local descent methods in federated learning
Haddadpour, F. and Mahdavi, M. (2019) · 1910
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Scaffold: Stochastic controlled averaging for on-device federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T. (2019) · 1910
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · 1912
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Gradient methods for minimizing functionals
Polyak, B. T. (1963) · 1963
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The mnist database of handwritten digits
LeCun, Y. (1998) · 1998
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Pac-bayesian model averaging
McAllester, D. A. (1999) · 1999
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Yang, J., Kiyavash, N., and He, N. (2020) · 2002
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Model-based robust deep learning
Robey, A., Hassani, H., and Pappas, G. J. (2020) · 2005
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P. (2017) · 2017
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Neyshabur, B., Bhojanapalli, S., and Srebro, N. (2017) · 2017
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Deepxplore: Automated whitebox testing of deep learning systems
Pei, K., Cao, Y., Yang, J., and Jana, S. (2017) · 2017
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Protection against reconstruction and its applications in private federated learning
Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., and Rogers, R. (2018) · 2018
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Expanding the reach of federated learning by reducing client resource requirements
Caldas, S., Konečny, J., McMahan, H. B., and Talwalkar, A. (2018) · 2018
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Distributionally robust convex optimization
Wiesemann, W., Kuhn, D., and Sim, M. (2014) · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015) · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D. (2016) · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al. (2016) · 2016
Cited alongside, same era.
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Generalizable adversarial training via spectral normalization
Farnia, F., Zhang, J. M., and Tse, D. (2018) · 2018
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Universal adversarial training
Shafahi, A., Najibi, M., Xu, Z., Dickerson, J., Davis, L. S., and Goldstein, T. (2018) · 2018
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Local sgd converges fast and communicates little
Stich, S. U. (2018) · 2018
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Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd
Wang, J. and Joshi, G. (2018) · 2018
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Decentralized stochastic optimization and gossip algorithms with compressed communication
Koloskova, A., Stich, S. U., and Jaggi, M. (2019) · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T. (2019) · 2019
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Solving a class of non-convex min-max games using iterative first order methods
Nouiehed, M., Sanjabi, M., Huang, T., Lee, J. D., and Razaviyayn, M. (2019) · 2019
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Regularization via mass transportation
Shafieezadeh-Abadeh, S., Kuhn, D., and Esfahani, P. M. (2019) · 2019
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Tighter theory for local sgd on identical and heterogeneous data
Khaled, A., Mishchenko, K., and Richtárik, P. (2020) · 2020
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