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Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices.
Robust Learning from Untrusted Sources
Konstantinov, N.; and Lampert, C. 2019 · 1901
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Differentially Private Learning with Adaptive Clipping
Thakkar, O.; Andrew, G.; and McMahan, H. B. 2019 · 1905
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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
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Introduction to Robust Estimation and Hypothesis Testing
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Gradient-based learning applied to document recognition
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Estimating a Dirichlet distribution
Minka, T. 2000 · 2000
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Poisoning Attacks against Support Vector Machines
Biggio, B.; Nelson, B.; and Laskov, P. 2012 · 2012
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Feng, J.; Xu, H.; and Mannor, S. 2014 · 2014
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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Bag of tricks for efficient text classification
Joulin, A.; Grave, E.; Bojanowski, P.; and Mikolov, T. 2016 · 2016
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
Ruining, H.; and Julian, M. 2016 · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P.; Guerraoui, R.; Stainer, J.; et al. 2017 · 2017
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K.; Ivanov, V.; Kreuter, B.; Marcedone, A.; McMahan, H. B.; Patel, S.; Ramage, D.; Segal, A.; and Seth, K. 2017 · 2017
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How to backdoor federated learning
Bagdasaryan, E.; Veit, A.; Hua, Y.; Estrin, D.; and Shmatikov, V. 2018 · 2018
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Mitigating sybils in federated learning poisoning
Fung, C.; Yoon, C. J.; and Beschastnikh, I. 2018 · 2018
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Reaching Data Confidentiality and Model Accountability on the CalTrain
Gu, Z.; Jamjoom, H.; Su, D.; Huang, H.; Zhang, J.; Ma, T.; Pendarakis, D.; and Molloy, I. 2018 · 2018
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A Hybrid Approach to Privacy-Preserving Federated Learning
Truex, S.; Baracaldo, N.; Anwar, A.; Steinke, T.; Ludwig, H.; and Zhang, R. 2018 · 2018
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Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
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Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
Chen, Y.; Su, L.; and Xu, J. 2017 · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Geyer, R. C.; Klein, T.; and Nabi, M. 2017 · 2017
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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B.; Ateniese, G.; and Pérez-Cruz, F. 2017 · 2017
Cited alongside, same era.
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Automatic differentiation in PyTorch
Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; and Lerer, A. 2017 · 2017
Cited alongside, same era.
Byzantine Stochastic Gradient Descent
Alistarh, D.; Allen-Zhu, Z.; and Li, J. 2018 · 2018
Cited alongside, same era.
Wang, Z.; Song, M.; Zhang, Z.; Song, Y.; Wang, Q.; and Qi, H. 2018 · 2018
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BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
Gu, T.; Liu, K.; Dolan-Gavitt, B.; and Garg, S. 2019 · 2019
Closest in time.
LOGAN: Membership inference attacks against generative models
Hayes, J.; Melis, L.; Danezis, G.; and De Cristofaro, E. 2019 · 2019
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Lending Club Loan Data
Kan, W. 2019 · 2019
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RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
Li, L.; Xu, W.; Chen, T.; Giannakis, G. B.; and Ling, Q. 2019 · 2019
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Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L.; Song, C.; Cristofaro, E. D.; and Shmatikov, V. 2019 · 2019
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