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Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzatine poisoning adversarial attacks.
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M. Nasr, R. Shokri, A. Houmansadr, · 2019
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H. Xiao, K. Rasul, R. Vollgraf, · 2017
Cited alongside, same era.
Certified defenses for data poisoning attacks,
J. Steinhardt, P. W. Koh, P. Liang, · 2017
Cited alongside, same era.
Machine learning with adversaries: Byzantine tolerant gradient descent,
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, J. Stainer, · 2017
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B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y Arcas, · 2017
Cited alongside, same era.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,
Y. Chen, L. Su, J. Xu, · 2017
Cited alongside, same era.
Manipulating machine learning: Poisoning attacks and countermeasures for regression learning,
M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, B. Li, · 2018
Cited alongside, same era.
Model poisoning attacks in federated learning,
A. N. Bhagoji, S. Chakraborty, P. Mittal, S. Calo, · 2018
Cited alongside, same era.
Efficient federated learning for cloud-based AIoT applications,
X. Zhang, M. Hu, J. Xia, T. Wei, M. Chen, S. Hu, · 2020
Closest in time.
How to backdoor federated learning,
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, V. Shmatikov, · 2020
Closest in time.
Data poisoning attacks against federated learning systems,
V. Tolpegin, S. Truex, M. E. Gursoy, L. Liu, · 2020
Closest in time.
Privacy and robustness in federated learning: Attacks and defenses,
L. Lyu, H. Yu, X. Ma, L. Sun, J. Zhao, Q. Yang, P. S. Yu, · 2020
Closest in time.
Data poisoning attacks on federated machine learning,
G. Sun, Y. Cong, J. Dong, Q. Wang, J. Liu, · 2020
Closest in time.
Outliers in official statistics,
K. Wada, · 2020
Closest in time.
Federated learning and differential privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy,
N. Rodríguez-Barroso, G. Stipcich, D. Jiménez-López, J. A. Ruiz-Millán, E. Martínez-Cámara, G. González-Seco, M. V. Luzón, M. Ángel Veganzones, F. Herrera, · 2020
Closest in time.
Flower: A friendly federated learning research framework,
D. J. Beutel, T. Topal, A. Mathur, X. Qiu, T. Parcollet, N. D. Lane, · 2020
Closest in time.
Advances and open problems in federated learning,
P. Kairouz, H. B. McMahan, · 2021
Closest in time.
Fedsa: A staleness-aware asynchronous federated learning algorithm with non-iid data,
M. Chen, B. Mao, T. Ma, · 2021
Closest in time.
A survey on security and privacy of federated learning,
V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, G. Srivastava, · 2021
Closest in time.
Exploring the limits of out-of-distribution detection,
S. Fort, J. Ren, B. Lakshminarayanan, · 2021
Closest in time.
Deep model poisoning attack on federated learning,
X. Zhou, M. Xu, Y. Wu, N. Zheng, · 2021
Closest in time.