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We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models.
1911
Earlier work this paper cites.
P. J. Huber, “Robust estimation of a location parameter,” The Annals of Mathematical Statistics , vol. 35, no. 1, pp. 73– 101, 1964
1964
Earlier work this paper cites.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural computation , 3(1):79–87, 1991
1991
Earlier work this paper cites.
A. Rida, A. Labbi, and C. Pellegrini, “Local experts combination through density decomposition,” in International Workshop on AI and Statistics, Uncertainty ’99 . Morgan Kaufmann, 1999
1999
Earlier work this paper cites.
R. Collobert, S. Bengio, and Y. Bengio, “A parallel mixture of SVMs for very large scale problems,” Neural Computation , vol. 14, no. 5, pp. 1105–1114, 2002
2002
Earlier work this paper cites.
P. McDaniel, N. Papernot, and Z. B. Celik, “Machine learning in adversarial settings,” IEEE Security Privacy , vol. 14, no. 3, pp. 68–72, 2016
2016
Earlier work this paper cites.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in AISTATS , 2016
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 119–129. [Online]. Available: http://papers.nips.cc/paper/6617-machine-learning-with-adversaries-byzantine-tolerant-gradient-descent.pdf
2017
Cited alongside, same era.
2017
Cited alongside, same era.
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” 2020
2020
Later among the works it cites.
C. Xie, K. Huang, P.-Y. Chen, and B. Li, “DBA: Distributed backdoor attacks against federated learning,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=rkgyS0VFvr
2020
Later among the works it cites.
A. Koloskova, N. Loizou, S. Boreiri, M. Jaggi, and S. U. Stich, “A unified theory of decentralized SGD with changing topology and local updates,” 2020
2020
Later among the works it cites.
B. E. Woodworth, K. K. Patel, and N. Srebro, “Minibatch vs local SGD for heterogeneous distributed learning,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 6281–6292. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/45713f6ff2041d3fdfae927b82488db8-Paper.pdf
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2018
Cited alongside, same era.
Q. Yang, Y. Liu, TChen, and Y. Tong, “Federated machine learning: Concept and applications,” Arxive , vol. 10, no. 2, pp. 12:1–12:19, February 2019. [Online]. Available: http://doi.acm.org/10.1145/3298981
2019
Cited alongside, same era.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” 2019
2019
Cited alongside, same era.
K. Pillutla, S. M. Kakade, and Z. Harchaoui, “Robust aggregation for federated learning,” 2019
2019
Cited alongside, same era.
2020
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2020
Later among the works it cites.
2020
Later among the works it cites.