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Federated Learning (FL) is a promising distributed learning approach that enables multiple clients to collaboratively train a shared global model.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” 1960
1960
Earlier work this paper cites.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,” Chemometrics and intelligent laboratory systems , vol. 2, no. 1-3, pp. 37–52, 1987
1987
Earlier work this paper cites.
P. J. Rousseeuw and C. Croux, “Alternatives to the median absolute deviation,” Journal of the American Statistical association , vol. 88, no. 424, pp. 1273–1283, 1993
1993
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , 1998
1998
Earlier work this paper cites.
T. Minka, “Estimating a dirichlet distribution,” 2000
2000
Earlier work this paper cites.
R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart, “The mahalanobis distance,” Chemometrics and intelligent laboratory systems , vol. 50, no. 1, pp. 1–18, 2000
2000
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in ICML , 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” NeurIPS , 2012
2012
Earlier work this paper cites.
S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel, “Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark,” in IJCNN , no. 1288, 2013
2013
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra Perez, and J. L. Reyes Ortiz, “A public domain dataset for human activity recognition using smartphones,” in ESANN , 2013
2013
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV , 2014
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv , 2014
2014
Earlier work this paper cites.
S. Shen, S. Tople, and P. Saxena, “Auror: Defending against poisoning attacks in collaborative deep learning systems,” in ACSAC , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in AISTATS , 2017
2017
Earlier work this paper cites.
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in NeurIPS , 2017
2017
Earlier work this paper cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” arXiv , 2017
2017
Earlier work this paper cites.
X. Chen, C. Liu, B. Li, K. Lu, and D. Song, “Targeted backdoor attacks on deep learning systems using data poisoning,” arXiv , 2017
2017
Earlier work this paper cites.
A. Egoyan, “Can someone differentiate between cosine, adjusted cosine, and pearson correlation similarity measuring techniques,” 2017
2017
Earlier work this paper cites.
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik, “Emnist: Extending mnist to handwritten letters,” in IJCNN . IEEE, 2017
2017
Earlier work this paper cites.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” POMACS , 2017
2017
Earlier work this paper cites.
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning differentially private recurrent language models,” arXiv , 2017
2017
Cited alongside, same era.
A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” arXiv , 2018
2018
Cited alongside, same era.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in NDSS , 2018
2018
Cited alongside, same era.
K. Liu, B. Dolan-Gavitt, and S. Garg, “Fine-pruning: Defending against backdooring attacks on deep neural networks,” in RAID , 2018
2018
Cited alongside, same era.
R. Guerraoui, S. Rouault et al. , “The hidden vulnerability of distributed learning in byzantium,” in ICML , 2018
2018
Cited alongside, same era.
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J.-y. Sohn, K. Lee, and D. Papailiopoulos, “Attack of the tails: Yes, you really can backdoor federated learning,” NeurIPS , 2020
2020
Later among the works it cites.
V. Shejwalkar and A. Houmansadr, “Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning,” in NDSS , 2021
2021
Later among the works it cites.
E. Bagdasaryan and V. Shmatikov, “Blind backdoors in deep learning models,” in USENIX , 2021
2021
Later among the works it cites.
H. Zhu, J. Xu, S. Liu, and Y. Jin, “Federated learning on non-iid data: A survey,” Neurocomputing , 2021
2021
Later among the works it cites.
X. Cao, M. Fang, J. Liu, and N. Z. Gong, “Fltrust: Byzantine-robust federated learning via trust bootstrapping,” in NDSS , 2021
2021
Later among the works it cites.
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D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in ICML , 2018
2018
Cited alongside, same era.
“Designing for privacy - wwdc19 - videos - apple developer,” https://developer.apple.com/videos/play/wwdc2019/708 , 2019
2019
Cited alongside, same era.
C. Xie, K. Huang, P.-Y. Chen, and B. Li, “Dba: Distributed backdoor attacks against federated learning,” in ICLR , 2019
2019
Cited alongside, same era.
L. Li, W. Xu, T. Chen, G. B. Giannakis, and Q. Ling, “Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,” in AAAI , 2019
2019
Cited alongside, same era.
M. Barni, K. Kallas, and B. Tondi, “A new backdoor attack in cnns by training set corruption without label poisoning,” in ICIP , 2019
2019
Cited alongside, same era.
G. Baruch, M. Baruch, and Y. Goldberg, “A little is enough: Circumventing defenses for distributed learning,” NeurIPS , 2019
2019
Cited alongside, same era.
Y. Liu, W.-C. Lee, G. Tao, S. Ma, Y. Aafer, and X. Zhang, “Abs: Scanning neural networks for back-doors by artificial brain stimulation,” in CCS , 2019
2019
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021
2021
Later among the works it cites.
H. Hashemi, Y. Wang, C. Guo, and M. Annavaram, “Byzantine-robust and privacy-preserving framework for fedml,” arXiv , 2021
2021
Later among the works it cites.
S. Awan, B. Luo, and F. Li, “Contra: Defending against poisoning attacks in federated learning,” in ESORICS , 2021
2021
Later among the works it cites.
Y. Li, X. Lyu, N. Koren, L. Lyu, B. Li, and X. Ma, “Neural attention distillation: Erasing backdoor triggers from deep neural networks,” in ICLR , 2021
2021
Later among the works it cites.
G. Shen, Y. Liu, G. Tao, S. An, Q. Xu, S. Cheng, S. Ma, and X. Zhang, “Backdoor scanning for deep neural networks through k-arm optimization,” in ICML , 2021
2021
Later among the works it cites.
S. Andreina, G. A. Marson, H. Möllering, and G. Karame, “Baffle: Backdoor detection via feedback-based federated learning,” in ICDCS , 2021
2021
Later among the works it cites.
X. Xu, Q. Wang, H. Li, N. Borisov, C. A. Gunter, and B. Li, “Detecting ai trojans using meta neural analysis,” in SP , 2021
2021
Later among the works it cites.
V. Shejwalkar, A. Houmansadr, P. Kairouz, and D. Ramage, “Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning,” in SP , 2022
2022
Later among the works it cites.
Z. Zhang, A. Panda, L. Song, Y. Yang, M. Mahoney, P. Mittal, R. Kannan, and J. Gonzalez, “Neurotoxin: durable backdoors in federated learning,” in ICML , 2022
2022
Later among the works it cites.
X. Qi, T. Xie, R. Pan, J. Zhu, Y. Yang, and K. Bu, “Towards practical deployment-stage backdoor attack on deep neural networks,” in CVPR , 2022
2022
Later among the works it cites.
T. D. Nguyen, P. Rieger, H. Chen, H. Yalame, H. Möllering, H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, S. Zeitouni et al. , “Flame: Taming backdoors in federated learning,” in USENIX , 2022
2022
Later among the works it cites.
A. Malki, E.-S. Atlam, and I. Gad, “Machine learning approach of detecting anomalies and forecasting time-series of iot devices,” Alexandria Engineering Journal , 2022
2022
Later among the works it cites.
Z. Zhang, X. Cao, J. Jia, and N. Z. Gong, “Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients,” in SIGKDD , 2022
2022
Later among the works it cites.
P. Rieger, T. D. Nguyen, M. Miettinen, and A.-R. Sadeghi, “Deepsight: Mitigating backdoor attacks in federated learning through deep model inspection,” in NDSS , 2022
2022
Later among the works it cites.
M. Naseri, J. Hayes, and E. De Cristofaro, “Local and central differential privacy for robustness and privacy in federated learning,” in NDSS , 2022
2022
Later among the works it cites.
X. Cao, J. Jia, Z. Zhang, and N. Z. Gong, “Fedrecover: Recovering from poisoning attacks in federated learning using historical information,” in SP , 2023
2023
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