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Byzantine robustness has received significant attention recently given its importance for distributed and federated learning.
Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
So, J., Guler, B., and Avestimehr, A. S · 2002
Earlier work this paper cites.
Byzantine-robust learning on heterogeneous datasets via resampling
He, L., Karimireddy, S. P., and Jaggi, M · 2006
Earlier work this paper cites.
Secure byzantine-robust machine learning
He, L., Karimireddy, S. P., and Jaggi, M · 2006
Earlier work this paper cites.
Byzantine-resilient secure federated learning
So, J., Guler, B., and Avestimehr, A. S · 2007
Earlier work this paper cites.
High-breakdown robust multivariate methods
Hubert, M., Rousseeuw, P. J., and Van Aelst, S · 2008
Earlier work this paper cites.
Mime: Mimicking centralized stochastic algorithms in federated learning
Karimireddy, S. P., Jaggi, M., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
Earlier work this paper cites.
Feng, J., Xu, H., and Mannor, S · 2014
Earlier work this paper cites.
Geometric median and robust estimation in banach spaces
Minsker, S. et al · 2015
Earlier work this paper cites.
Revisiting distributed synchronous sgd
Chen, J., Pan, X., Monga, R., Bengio, S., and Jozefowicz, R · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Agnostic estimation of mean and covariance
Lai, K. A., Rao, A. B., and Vempala, S · 2016
Earlier work this paper cites.
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Blanchard, P., El Mhamdi, E. M., Guerraoui, R., and Stainer, J · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Distributed statistical machine learning in adversarial settings
Chen, Y., Su, L., and Xu, J · 2017
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Earlier work this paper cites.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Hoffer, E., Hubara, I., and Soudry, D · 2017
Earlier work this paper cites.
Byzantine stochastic gradient descent
Alistarh, D., Allen-Zhu, Z., and Li, J · 2018
Earlier work this paper cites.
signSGD with majority vote is communication efficient and fault tolerant
Bernstein, J., Zhao, J., Azizzadenesheli, K., and Anandkumar, A · 2018
Earlier work this paper cites.
Draco: Byzantine-resilient distributed training via redundant gradients
Chen, L., Wang, H., Charles, Z., and Papailiopoulos, D · 2018
Earlier work this paper cites.
Data encoding for byzantine-resilient distributed gradient descent
Data, D., Song, L., and Diggavi, S · 2018
Cited alongside, same era.
Sever: A robust meta-algorithm for stochastic optimization
Diakonikolas, I., Kamath, G., Kane, D. M., Li, J., Steinhardt, J., and Stewart, A · 2018
Cited alongside, same era.
The hidden vulnerability of distributed learning in byzantium
Mhamdi, E. M. E., Guerraoui, R., and Rouault, S · 2018
Cited alongside, same era.
Measuring the effects of data parallelism on neural network training
Shallue, C. J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G. E · 2018
Cited alongside, same era.
Securing distributed gradient descent in high dimensional statistical learning
Su, L. and Xu, J · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Yin, D., Chen, Y., Ramchandran, K., and Bartlett, P · 2018
Detox: A redundancy-based framework for faster and more robust gradient aggregation
Rajput, S., Wang, H., Charles, Z., and Papailiopoulos, D · 2019
Later among the works it cites.
Can you really backdoor federated learning?
Sun, Z., Kairouz, P., Suresh, A. T., and McMahan, H. B · 2019
Later among the works it cites.
On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization
Yu, H., Jin, R., and Yang, S · 2019
Later among the works it cites.
Why adam beats sgd for attention models
Zhang, J., Karimireddy, S. P., Veit, A., Kim, S., Reddi, S. J., Kumar, S., and Sra, S · 2019
Later among the works it cites.
Byzantine-resilient sgd in high dimensions on heterogeneous data
Data, D. and Diggavi, S · 2020
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Cited alongside, same era.
Lower bounds for non-convex stochastic optimization
Arjevani, Y., Carmon, Y., Duchi, J. C., Foster, D. J., Srebro, N., and Woodworth, B · 2019
Cited alongside, same era.
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2019
Cited alongside, same era.
A little is enough: Circumventing defenses for distributed learning
Baruch, G., Baruch, M., and Goldberg, Y · 2019
Cited alongside, same era.
Distributed training with heterogeneous data: Bridging median- and mean-based algorithms
Chen, X., Chen, T., Sun, H., Wu, Z. S., and Hong, M · 2019
Cited alongside, same era.
Momentum-based variance reduction in non-convex sgd
Cutkosky, A. and Orabona, F · 2019
Cited alongside, same era.
Data encoding for byzantine-resilient distributed optimization
Data, D., Song, L., and Diggavi, S · 2019
Cited alongside, same era.
Dong, Y., Giannakis, G. B., Chen, T., Cheng, J., Hossain, M. J., and Leung, V. C. M · 2020
Closest in time.
Collaborative learning as an agreement problem
El-Mhamdi, E.-M., Guerraoui, R., Guirguis, A., Hoang, L. N., and Rouault, S · 2020
Closest in time.
Stochastic optimization with heavy-tailed noise via accelerated gradient clipping
Gorbunov, E., Danilova, M., and Gasnikov, A · 2020
Closest in time.
Stochastic-sign sgd for federated learning with theoretical guarantees
Jin, R., Huang, Y., He, X., Wu, T., and Dai, H · 2020
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Byzshield: An efficient and robust system for distributed training
Konstantinidis, K. and Ramamoorthy, A · 2020
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An improved analysis of stochastic gradient descent with momentum
Liu, Y., Gao, Y., and Yin, W · 2020
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Byzantine-robust decentralized stochastic optimization
Peng, J. and Ling, Q · 2020
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Byzantine-robust variance-reduced federated learning over distributed non-i.i.d. data
Peng, J., Wu, Z., and Ling, Q · 2020
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Befriending the byzantines through reputation scores
Regatti, J. and Gupta, A · 2020
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Dynamic federated learning model for identifying adversarial clients
Rodríguez-Barroso, N., Martínez-Cámara, E., Luzón, M. V., Seco, G. G., Ángel Veganzones, M., and Herrera, F · 2020
Closest in time.
Hybrid variance-reduced sgd algorithms for nonconvex-concave minimax problems
Tran-Dinh, Q., Liu, D., and Nguyen, L. M · 2020
Closest in time.
Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-y., Lee, K., and Papailiopoulos, D · 2020
Closest in time.
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
Xie, C., Koyejo, O., and Gupta, I · 2020
Closest in time.
Byzantine-resilient non-convex stochastic gradient descent
Allen-Zhu, Z., Ebrahimian, F., Li, J., and Alistarh, D · 2021
Closest in time.
Distributed momentum for byzantine-resilient learning
El-Mhamdi, E.-M., Guerraoui, R., and Rouault, S · 2021
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